subject:(unsupervised learning)

1. Kocher, MIrco. Text clustering with styles.

Degree: 2017, Université de Neuchâtel

► Cette thèse présente le problème du regroupement d'auteurs formulé de la manière suivante : en partant d'un ensemble composé de n textes, le but est… (more)

▼ Cette thèse présente le problème du regroupement d'auteurs formulé de la manière suivante : en partant d'un ensemble composé de n textes, le but est de déterminer le nombre k d'auteurs distincts, pour regrouper les textes en k classes. De manière itérative, nous construisons un système stable et simple qui est capable de regrouper automatiquement les documents selon leurs thèmes. Dans notre étude, nous commençons par proposer une mesure capable d'estimer l'(in-)certitude de la décision proposée, dans le but d'obtenir un indicateur de confiance en lieu et place d'une simple réponse. Ensuite, nous combinons les paires de textes pour lesquelles une même affectation apparaît, et dont nous sommes suffisamment confiants pour affirmer qu'ils sont rédigés par le même auteur. Enfin, après avoir vérifié chaque tuple de textes, nous construisons les classes en nous basant sur une stratégie utilisant une distance entre distributions probabilistes. Grâce à l'utilisation d'une limite dynamique, nous sommes à même de choisir les plus petites distances relatives pour détecter une origine commune entre textes. Bien que notre étude se concentre principalement sur la création de méthodes simples, des schémas plus complexes mènent à des résultats plus performants. Ainsi, nous avons opté pour une représentation distribuée et nous avons comparé son efficacité à plusieurs méthodes d'attribution d'auteurs. Cette évaluation nous permet de démontrer que toutes les approches n'excellent pas dans toutes les situations, et que des méthodes d'apprentissage profond peuvent être sensibles au choix des paramètres. Les observations les plus proches des exemples en question (ou la catégorie ayant la plus petite distance) déterminent généralement les réponses proposées. Nous avons testé plusieurs fonctions de distance inter-textuelle sur des critères théoriques et empiriques. Nous démontrons que les distances dites de Tanimoto et de Matusita respectent toutes les propriétés théoriques. Toutes deux obtiennent également de bons résultats dans le cadre de tests empiriques. Toutefois, les mesures de Canberra et de Clark sont encore mieux adaptées, bien qu'elles ne remplissent pas toutes les caractéristiques théoriques demandées. De manière générale, l'on constate que la fonction Cosinus ne répond pas à toutes les conditions, et se comporte de façon suboptimale. Enfin, nous observons que la réduction des traits stylistiques retenues diminue le temps d'exécution et peut également améliorer les performances en ignorant les redondantes. Nous testons nos systèmes pour différentes langues naturelles appartenant à une variété de familles de langues et pour plusieurs genres de textes. Grâce à la sélection flexible des attributs, nos systèmes sont capables de produire des résultats fiables dans toutes les conditions testées. Advisors/Committee Members: Jacques (Dir.).

Subjects/Keywords: unsupervised learning

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APA (6^th Edition):

Kocher, M. (2017). Text clustering with styles. (Thesis). Université de Neuchâtel. Retrieved from http://doc.rero.ch/record/306696

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Kocher, MIrco. “Text clustering with styles.” 2017. Thesis, Université de Neuchâtel. Accessed February 26, 2020. http://doc.rero.ch/record/306696.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Kocher, MIrco. “Text clustering with styles.” 2017. Web. 26 Feb 2020.

Vancouver:

Kocher M. Text clustering with styles. [Internet] [Thesis]. Université de Neuchâtel; 2017. [cited 2020 Feb 26]. Available from: http://doc.rero.ch/record/306696.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kocher M. Text clustering with styles. [Thesis]. Université de Neuchâtel; 2017. Available from: http://doc.rero.ch/record/306696

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Victoria University of Wellington

2. Butler-Yeoman, Tony. Learning to Disentangle the Complex Causes of Data.

Degree: 2017, Victoria University of Wellington

URL: http://hdl.handle.net/10063/6951

► The ability to extract and model the meaning in data has been key to the success of modern machine learning. Typically, data reflects a combination… (more)

Subjects/Keywords: Unsupervised; Machine; Learning

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APA (6^th Edition):

Butler-Yeoman, T. (2017). Learning to Disentangle the Complex Causes of Data. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/6951

Chicago Manual of Style (16^th Edition):

Butler-Yeoman, Tony. “Learning to Disentangle the Complex Causes of Data.” 2017. Masters Thesis, Victoria University of Wellington. Accessed February 26, 2020. http://hdl.handle.net/10063/6951.

MLA Handbook (7^th Edition):

Butler-Yeoman, Tony. “Learning to Disentangle the Complex Causes of Data.” 2017. Web. 26 Feb 2020.

Vancouver:

Butler-Yeoman T. Learning to Disentangle the Complex Causes of Data. [Internet] [Masters thesis]. Victoria University of Wellington; 2017. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/10063/6951.

Council of Science Editors:

Butler-Yeoman T. Learning to Disentangle the Complex Causes of Data. [Masters Thesis]. Victoria University of Wellington; 2017. Available from: http://hdl.handle.net/10063/6951

3. Aversano, Gianmarco. Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.

Degree: Docteur es, Combustion, 2019, Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....)

URL: http://www.theses.fr/2019SACLC095

► L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées… (more)

▼ L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées et combinées dans les travaux de la présente thèse pour l’extraction de caractéristiques et la construction de modèles d’ordre réduit. Ainsi, l’application de techniques pilotées par les données pour la détection des caractéristiques d’ensembles de données de combustion turbulente (simulation numérique directe) a été étudiée sur deux flammes H2 / CO: une évolution spatiale (DNS1) et une jet à évolution temporelle (DNS2). Des méthodes telles que l’analyse en composantes principales (ACP), l’analyse en composantes principales locales (LPCA), la factorisation matricielle non négative (NMF) et les autoencodeurs ont été explorées à cette fin. Il a été démontré que divers facteurs pouvaient affecter les performances de ces méthodes, tels que les critères utilisés pour le centrage et la mise à l’échelle des données d’origine ou le choix du nombre de dimensions dans les approximations de rang inférieur. Un ensemble de lignes directrices a été présenté qui peut aider le processus d’identification de caractéristiques physiques significatives à partir de données de flux réactifs turbulents. Des méthodes de compression de données telles que l’analyse en composantes principales (ACP) et les variations ont été combinées à des méthodes d’interpolation telles que le krigeage, pour la construction de modèles ordonnées à prix réduits et calculables pour la prédiction de l’état d’un système de combustion dans des conditions de fonctionnement inconnues ou des combinaisons de modèles valeurs de paramètre d’entrée. La méthodologie a d’abord été testée pour la prévision des flammes 1D avec un nombre croissant de paramètres d’entrée (rapport d’équivalence, composition du carburant et température d’entrée), avec des variantes de l’approche PCA classique, à savoir PCA contrainte et PCA locale, appliquée aux cas de combustion la première fois en combinaison avec une technique d’interpolation. Les résultats positifs de l’étude ont conduit à l’application de la méthodologie proposée aux flammes 2D avec deux paramètres d’entrée, à savoir la composition du combustible et la vitesse d’entrée, qui ont donné des résultats satisfaisants. Des alternatives aux méthodes non supervisées et supervisées choisies ont également été testées sur les mêmes données 2D. L’utilisation de la factorisation matricielle non négative (FNM) pour l’approximation de bas rang a été étudiée en raison de la capacité de la méthode à représenter des données à valeur positive, ce qui permet de ne pas enfreindre des lois physiques importantes telles que la positivité des fractions de masse d’espèces chimiques et comparée à la PCA. Comme méthodes supervisées alternatives, la combinaison de l’expansion du chaos polynomial (PCE) et du Kriging et l’utilisation de réseaux de neurones artificiels (RNA) ont été testées. Les résultats des travaux susmentionnés ont ouvert la voie au développement d’un… Advisors/Committee Members: Gicquel, Olivier (thesis director), Parente, Alessandro (thesis director).

Subjects/Keywords: Combustion; Unsupervised learning; Supervised learning; Combustion; Unsupervised learning; Supervised learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Aversano, G. (2019). Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. (Doctoral Dissertation). Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....). Retrieved from http://www.theses.fr/2019SACLC095

Chicago Manual of Style (16^th Edition):

Aversano, Gianmarco. “Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.” 2019. Doctoral Dissertation, Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....). Accessed February 26, 2020. http://www.theses.fr/2019SACLC095.

MLA Handbook (7^th Edition):

Aversano, Gianmarco. “Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.” 2019. Web. 26 Feb 2020.

Vancouver:

Aversano G. Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....); 2019. [cited 2020 Feb 26]. Available from: http://www.theses.fr/2019SACLC095.

Council of Science Editors:

Aversano G. Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....); 2019. Available from: http://www.theses.fr/2019SACLC095

4. Hirayama, Jun-ichiro. Probabilistic approach to unsupervised representation learning in dynamic environments : 動的環境における教師なし表現学習への確率的アプローチ; ドウテキカンキョウニオケルキョウシナシヒョウゲンガクシュウエノカクリツテキアプローチ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/4366

Subjects/Keywords: unsupervised learning

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APA (6^th Edition):

Hirayama, J. (n.d.). Probabilistic approach to unsupervised representation learning in dynamic environments : 動的環境における教師なし表現学習への確率的アプローチ; ドウテキカンキョウニオケルキョウシナシヒョウゲンガクシュウエノカクリツテキアプローチ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/4366

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hirayama, Jun-ichiro. “Probabilistic approach to unsupervised representation learning in dynamic environments : 動的環境における教師なし表現学習への確率的アプローチ; ドウテキカンキョウニオケルキョウシナシヒョウゲンガクシュウエノカクリツテキアプローチ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 26, 2020. http://hdl.handle.net/10061/4366.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hirayama, Jun-ichiro. “Probabilistic approach to unsupervised representation learning in dynamic environments : 動的環境における教師なし表現学習への確率的アプローチ; ドウテキカンキョウニオケルキョウシナシヒョウゲンガクシュウエノカクリツテキアプローチ.” Web. 26 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Hirayama J. Probabilistic approach to unsupervised representation learning in dynamic environments : 動的環境における教師なし表現学習への確率的アプローチ; ドウテキカンキョウニオケルキョウシナシヒョウゲンガクシュウエノカクリツテキアプローチ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 26]. Available from: http://hdl.handle.net/10061/4366.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Hirayama J. Probabilistic approach to unsupervised representation learning in dynamic environments : 動的環境における教師なし表現学習への確率的アプローチ; ドウテキカンキョウニオケルキョウシナシヒョウゲンガクシュウエノカクリツテキアプローチ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/4366

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

5. Madokoro, Hirokazu. Unsupervised Category Formation and Its Applications to Robot Vision : 教師なしカテゴリ形成とロボットビジョンへの応用; キョウシナシカテゴリケイセイトロボットビジョンエノオウヨウ.

Degree: Nara Institute of Science and Technology / 奈良先端科学技術大学院大学

URL: http://hdl.handle.net/10061/6019

Subjects/Keywords: Unsupervised learning

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APA (6^th Edition):

Madokoro, H. (n.d.). Unsupervised Category Formation and Its Applications to Robot Vision : 教師なしカテゴリ形成とロボットビジョンへの応用; キョウシナシカテゴリケイセイトロボットビジョンエノオウヨウ. (Thesis). Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Retrieved from http://hdl.handle.net/10061/6019

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Madokoro, Hirokazu. “Unsupervised Category Formation and Its Applications to Robot Vision : 教師なしカテゴリ形成とロボットビジョンへの応用; キョウシナシカテゴリケイセイトロボットビジョンエノオウヨウ.” Thesis, Nara Institute of Science and Technology / 奈良先端科学技術大学院大学. Accessed February 26, 2020. http://hdl.handle.net/10061/6019.

Note: this citation may be lacking information needed for this citation format:
No year of publication.
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Madokoro, Hirokazu. “Unsupervised Category Formation and Its Applications to Robot Vision : 教師なしカテゴリ形成とロボットビジョンへの応用; キョウシナシカテゴリケイセイトロボットビジョンエノオウヨウ.” Web. 26 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
No year of publication.

Vancouver:

Madokoro H. Unsupervised Category Formation and Its Applications to Robot Vision : 教師なしカテゴリ形成とロボットビジョンへの応用; キョウシナシカテゴリケイセイトロボットビジョンエノオウヨウ. [Internet] [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; [cited 2020 Feb 26]. Available from: http://hdl.handle.net/10061/6019.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

Council of Science Editors:

Madokoro H. Unsupervised Category Formation and Its Applications to Robot Vision : 教師なしカテゴリ形成とロボットビジョンへの応用; キョウシナシカテゴリケイセイトロボットビジョンエノオウヨウ. [Thesis]. Nara Institute of Science and Technology / 奈良先端科学技術大学院大学; Available from: http://hdl.handle.net/10061/6019

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
No year of publication.

University of Guelph

6. Im, Jiwoong. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems .

Degree: 2015, University of Guelph

URL: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809

► The objective of this thesis is to take the dynamical systems approach to understand the unsupervised learning models and learning algorithms. Gated auto-encoders (GAEs) are… (more)

Subjects/Keywords: Machine learning; Deep Learning; unsupervised learning

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APA (6^th Edition):

Im, J. (2015). Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Im, Jiwoong. “Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems .” 2015. Thesis, University of Guelph. Accessed February 26, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Im, Jiwoong. “Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems .” 2015. Web. 26 Feb 2020.

Vancouver:

Im J. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems . [Internet] [Thesis]. University of Guelph; 2015. [cited 2020 Feb 26]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Im J. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems . [Thesis]. University of Guelph; 2015. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Alberta

7. White, Martha. Regularized factor models.

Degree: PhD, Department of Computing Science, 2014, University of Alberta

URL: https://era.library.ualberta.ca/files/gq67jt70x

► This dissertation explores regularized factor models as a simple unification of machine learn- ing problems, with a focus on algorithmic development within this known formalism.… (more)

▼ This dissertation explores regularized factor models as a simple unification of machine learn- ing problems, with a focus on algorithmic development within this known formalism. The main contributions are (1) the development of generic, efficient algorithms for a subclass of regularized factorizations and (2) new unifications that facilitate application of these algorithms to problems previously without known tractable algorithms. Concurrently, the generality of the formalism is further demonstrated with a thorough summary of known, but often scattered, connections between supervised and unsupervised learning problems and algorithms. The dissertation first presents the main algorithmic advances: convex reformulations of non- convex regularized factorization objectives. A convex reformulation is developed for a general subset of regularized factor models, with an efficiently computable optimization for five different regularization choices. The thesis then describes advances using these generic convex reformulation techniques in three important problems: multi-view subspace learning, semi-supervised learn- ing and estimating autoregressive moving average models. These novel settings are unified under regularized factor models by incorporating problem properties in terms of regularization. Once ex- pressed as regularized factor models, we can take advantage of the convex reformulation techniques to obtain novel algorithms that produce global solutions. These advances include the first global estimation procedure for two-view subspace learning and for autoregressive moving average models. The simple algorithms obtained from these general convex reformulation techniques are empirically shown to be effective across these three problems on a variety of datasets. This dissertation illustrates that many problems can be specified as a simple regularized factorization, that this class is amenable to global optimization and that it is advantageous to represent machine learning problems as regularized factor models.

Subjects/Keywords: machine learning; artificial intelligence; unsupervised learning

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APA (6^th Edition):

White, M. (2014). Regularized factor models. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/gq67jt70x

Chicago Manual of Style (16^th Edition):

White, Martha. “Regularized factor models.” 2014. Doctoral Dissertation, University of Alberta. Accessed February 26, 2020. https://era.library.ualberta.ca/files/gq67jt70x.

MLA Handbook (7^th Edition):

White, Martha. “Regularized factor models.” 2014. Web. 26 Feb 2020.

Vancouver:

White M. Regularized factor models. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2020 Feb 26]. Available from: https://era.library.ualberta.ca/files/gq67jt70x.

Council of Science Editors:

White M. Regularized factor models. [Doctoral Dissertation]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/gq67jt70x

University of Debrecen

8. Bod, Gergely. Self-taught learning: Implementation using MATLAB .

Degree: DE – TEK – Informatikai Kar, 2014, University of Debrecen

URL: http://hdl.handle.net/2437/178637

► Self-taught learning is a new framework in the domain of machine learning. It has the potential that by using unsupervised learning strategies to automatically learn… (more)

Subjects/Keywords: unsupervised learning; machine learning; neural network; autoencoder

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APA (6^th Edition):

Bod, G. (2014). Self-taught learning: Implementation using MATLAB . (Thesis). University of Debrecen. Retrieved from http://hdl.handle.net/2437/178637

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Bod, Gergely. “Self-taught learning: Implementation using MATLAB .” 2014. Thesis, University of Debrecen. Accessed February 26, 2020. http://hdl.handle.net/2437/178637.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Bod, Gergely. “Self-taught learning: Implementation using MATLAB .” 2014. Web. 26 Feb 2020.

Vancouver:

Bod G. Self-taught learning: Implementation using MATLAB . [Internet] [Thesis]. University of Debrecen; 2014. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/2437/178637.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bod G. Self-taught learning: Implementation using MATLAB . [Thesis]. University of Debrecen; 2014. Available from: http://hdl.handle.net/2437/178637

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of New South Wales

9. Xu, Jie. On-line and unsupervised learning for codebook based visual recognition.

Degree: Computer Science & Engineering, 2011, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true

► In this thesis we develop unsupervised and on-line learning algorithmsfor codebook based visual recognition tasks. First, we study the Prob-abilistic Latent Semantic Analysis (PLSA), which… (more)

▼ In this thesis we develop unsupervised and on-line learning algorithmsfor codebook based visual recognition tasks. First, we study the Prob-abilistic Latent Semantic Analysis (PLSA), which is one instance ofcodebook based recognition models. It has been successfully appliedto visual recognition tasks, such as image categorization, action recog-nition, etc. However it has been learned mainly in batch mode, andtherefore it cannot handle the data that arrives sequentially. We pro-pose a novel on-line learning algorithm for learning the parameters ofthe PLSA under that situation. Our contributions are two-fold: (i)an on-line learning algorithm that learns the parameters of the PLSAmodel from incoming data; (ii) a codebook adaptation algorithm thatcan capture the full characteristics of all features during the learn-ing. Experimental results demonstrate that the proposed algorithmcan handle sequentially arriving data that the batch PLSA learningcannot cope with.We then look at the Implicit Shape Model (ISM) for object detec-tion. ISM is a codebook based model in which object information isretained in codebooks. Existing ISM based methods require manuallabeling of training data. We propose an algorithm that can label thetraining data automatically. We also propose a method for identify-ing moving edges in video frames so that object hypotheses can begenerated only from the moving edges. We compare the proposed al-gorithm with a background subtraction based moving object detectionalgorithm. The experimental results demonstrate that the proposedalgorithm achieves comparable performance to the background sub-traction based counterpart, and it even outperforms the counterpartin complex situations.We then extend the aforementioned batch algorithm for on-line learn-ing. We propose an on-line training data collection algorithm and alsoan on-line codebook based object detector. We evaluate the algorithmon three video datasets. The experimental results demonstrate thatour algorithm outperforms the state-of-the-art on-line conservativelearning algorithm. Advisors/Committee Members: Wang, Yang, Computer Science & Engineering, Faculty of Engineering, UNSW, Wang, Wei, Computer Science & Engineering, Faculty of Engineering, UNSW, Ye, Getian, Computer Science & Engineering, Faculty of Engineering, UNSW.

Subjects/Keywords: Visual recognition; Online learning; Unsupervised learning

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APA (6^th Edition):

Xu, J. (2011). On-line and unsupervised learning for codebook based visual recognition. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Xu, Jie. “On-line and unsupervised learning for codebook based visual recognition.” 2011. Doctoral Dissertation, University of New South Wales. Accessed February 26, 2020. http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Xu, Jie. “On-line and unsupervised learning for codebook based visual recognition.” 2011. Web. 26 Feb 2020.

Vancouver:

Xu J. On-line and unsupervised learning for codebook based visual recognition. [Internet] [Doctoral dissertation]. University of New South Wales; 2011. [cited 2020 Feb 26]. Available from: http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true.

Council of Science Editors:

Xu J. On-line and unsupervised learning for codebook based visual recognition. [Doctoral Dissertation]. University of New South Wales; 2011. Available from: http://handle.unsw.edu.au/1959.4/51513 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10200/SOURCE02?view=true

University of Connecticut

10. Yankee, Tara N. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.

Degree: M. Eng., Biomedical Engineering, 2017, University of Connecticut

URL: https://opencommons.uconn.edu/gs_theses/1123

► The ability to collect and store large amounts of data is transforming data-driven discovery; recent technological advances in biology allow systematic data production and… (more)

Subjects/Keywords: clustering; ensemble learning; feature selection; unsupervised learning

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APA (6^th Edition):

Yankee, T. N. (2017). Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/1123

Chicago Manual of Style (16^th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Masters Thesis, University of Connecticut. Accessed February 26, 2020. https://opencommons.uconn.edu/gs_theses/1123.

MLA Handbook (7^th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Web. 26 Feb 2020.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2020 Feb 26]. Available from: https://opencommons.uconn.edu/gs_theses/1123.

Council of Science Editors:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Masters Thesis]. University of Connecticut; 2017. Available from: https://opencommons.uconn.edu/gs_theses/1123

University of Texas – Austin

11. -6888-3095. Embodied learning for visual recognition.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Texas – Austin

URL: http://hdl.handle.net/2152/63489

► The field of visual recognition in recent years has come to rely on large expensively curated and manually labeled "bags of disembodied images". In the… (more)

Subjects/Keywords: Computer vision; Unsupervised learning; Embodied learning

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APA (6^th Edition):

-6888-3095. (2017). Embodied learning for visual recognition. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63489

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed February 26, 2020. http://hdl.handle.net/2152/63489.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Web. 26 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-6888-3095. Embodied learning for visual recognition. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/2152/63489.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-6888-3095. Embodied learning for visual recognition. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63489

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

University of Illinois – Urbana-Champaign

12. Riaz, Mehwish. An unsupervised approach to identifying causal relations from relevant scenarios.

Degree: MS, 0112, 2010, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/14759

► Semantic relations between various text units play an important role in natural language understanding, as key elements of text coherence. The automatic identification of these… (more)

Subjects/Keywords: Causality; Semantic Relations; Topics; Unsupervised Learning

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APA (6^th Edition):

Riaz, M. (2010). An unsupervised approach to identifying causal relations from relevant scenarios. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/14759

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Riaz, Mehwish. “An unsupervised approach to identifying causal relations from relevant scenarios.” 2010. Thesis, University of Illinois – Urbana-Champaign. Accessed February 26, 2020. http://hdl.handle.net/2142/14759.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Riaz, Mehwish. “An unsupervised approach to identifying causal relations from relevant scenarios.” 2010. Web. 26 Feb 2020.

Vancouver:

Riaz M. An unsupervised approach to identifying causal relations from relevant scenarios. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2010. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/2142/14759.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Riaz M. An unsupervised approach to identifying causal relations from relevant scenarios. [Thesis]. University of Illinois – Urbana-Champaign; 2010. Available from: http://hdl.handle.net/2142/14759

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

13. Deutsch, Shay. Learning the geometric structure of high dimensional data using the Tensor Voting Graph.

Degree: PhD, Computer Science, 2017, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787

► This study addresses a range of fundamental problems in unsupervised manifold learning. Given a set of noisy points in a high dimensional space that lie… (more)

▼ This study addresses a range of fundamental problems in unsupervised manifold learning. Given a set of noisy points in a high dimensional space that lie near one or more possibly intersecting smooth manifolds, different challenges include learning the local geometric structure at each point, geodesic distance estimation, and clustering. These challenges are ubiquitous in unsupervised manifold learning, and many applications in computer vision as well as other scientific applications would benefit from a principled approach to these problems. ❧ In the first part of this thesis we present a hybrid local-global method that leverages the algorithmic capabilities of the Tensor Voting framework. However, unlike Tensor Voting, which can learn complex structures reliably only locally, our method is capable of reliably inferring the global structure of complex manifolds using a unique graph construction called the Tensor Voting Graph (TVG). This graph provides an efficient tool to perform the desired global manifold learning tasks such as geodesic distance estimation and clustering on complex manifolds, thus overcoming one of one of the main limitations of Tensor Voting as a strictly local approach. Moreover, we propose to explicitly and directly resolve the ambiguities near the intersections with a novel algorithm, which uses the TVG and the positions of the points near the manifold intersections. ❧ In the second part of this thesis we propose a new framework for manifold denoising based processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. The suggested approach, called MFD, uses the Spectral Graph Wavelet transform in order to perform non-iterative denoising directly in the graph frequency domain. To the best of our knowledge, MFD is the first attempt to use graph signal processing [55] tools for manifold denoising on unstructured domains. We provide theoretical justification for our Manifold Frequency Denoising approach on unstructured graphs and demonstrate that for smooth manifolds the coordinate signals also exhibit smoothness. This is first demonstrated in the case of noiseless observations, by proving that manifolds with smoother characteristics create more energy in the lower frequencies. Moreover, it is shown that higher frequency wavelet coefficients decay in a way that depends on the smoothness properties of the manifold, which is explicitly tied to the curvature properties. We then provide an analysis for the case of noisy points and a noisy graph, establishing results which tie the noisy graph Laplacian to the noiseless graph Laplacian characteristics that are induced by the smoothness properties of the manifold. The suggested MFD framework holds attractive features such as robustness to k nearest neighbors parameter selection on the graph, and it is computationally efficient. ❧ Finally, the last part of this research merges the Manifold Frequency Denoising and the Tensor Voting Graph methods into a uniform framework, which allows us to… Advisors/Committee Members: Medioni, Gerard (Committee Chair), Ortega, Antonio (Committee Member), Nakano, Aiichiro (Committee Member).

Subjects/Keywords: manifold learning; unsupervised denoising; Tensor Voting Graph

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APA (6^th Edition):

Deutsch, S. (2017). Learning the geometric structure of high dimensional data using the Tensor Voting Graph. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787

Chicago Manual of Style (16^th Edition):

Deutsch, Shay. “Learning the geometric structure of high dimensional data using the Tensor Voting Graph.” 2017. Doctoral Dissertation, University of Southern California. Accessed February 26, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787.

MLA Handbook (7^th Edition):

Deutsch, Shay. “Learning the geometric structure of high dimensional data using the Tensor Voting Graph.” 2017. Web. 26 Feb 2020.

Vancouver:

Deutsch S. Learning the geometric structure of high dimensional data using the Tensor Voting Graph. [Internet] [Doctoral dissertation]. University of Southern California; 2017. [cited 2020 Feb 26]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787.

Council of Science Editors:

Deutsch S. Learning the geometric structure of high dimensional data using the Tensor Voting Graph. [Doctoral Dissertation]. University of Southern California; 2017. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3787

University of Manchester

14. Rostamniakankalhori, Sharareh. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.

Degree: 2011, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

► Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision… (more)

▼ Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision and support is an element of the global plan to stop TB designed by the World Health Organization in 2006. The plan requires prediction of patient treatment course destination. The prediction outcome can be used to determine how intensive the level of supplying services and supports in frame of DOTS therapy should be. No predictive model for the outcome has been developed yet and only limited reports of influential factors for considered outcome are available.To fill this gap, this thesis develops a machine learning approach to predict the outcome of tuberculosis treatment course, which includes, firstly, data of 6,450 Iranian TB patients under DOTS (directly observed treatment, short course ) therapy were analysed to initially diagnose the significant predictors by correlation analysis; secondly, these significant features were applied to find the best classification approach from six examined algorithms including decision tree, Bayesian network, logistic regression, multilayer perceptron, radial basis function, and support vector machine; thirdly, the prediction accuracy of these existing techniques was improved by proposing and developing a new integrated method of k-mean clustering and classification algorithms. Finally, a cluster-based simplified decision tree (CSDT) was developed through an innovative hierarchical clustering and classification algorithm. CSDT was built by k-mean partitioning and the decision tree learning. This innovative method not only improves the prediction accuracy significantly but also leads to a much simpler and interpretative decision tree.The main results of this study included, firstly, finding seventeen significantly correlated features which were: age, sex, weight, nationality, area of residency, current stay in prison, low body weight, TB type, treatment category, length of disease, TB case type, recent TB infection, diabetic or HIV positive, and social risk factors like history of imprisonment, IV drug usage, and unprotected sex ; secondly, the results by applying and comparing six applied supervised machine learning tools on the testing set revealed that decision trees gave the best prediction accuracy (74.21%) compared with other methods; thirdly, by using testing set, the new integrated approach to combine the clustering and classification approach leads to the prediction accuracy improvement for all applied classifiers; the most and least improvement for prediction accuracy were shown by logistic regression (10%) and support vector machine (4%) respectively. Finally, by applying the proposed and developed CSDT, cluster-based simplified decision trees were optioned, which reduced the size of the resulting decision tree and further improved the prediction accuracy.Data type and having normal distribution have created an opportunity for the decision tree to outperform other algorithms. Pre-learning by k-mean… Advisors/Committee Members: Zeng, Xiaojun.

Subjects/Keywords: Integrated Supervised and Unsupervised Learning; Tuberculosis; plediction

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APA (6^th Edition):

Rostamniakankalhori, S. (2011). Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

Chicago Manual of Style (16^th Edition):

Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.” 2011. Doctoral Dissertation, University of Manchester. Accessed February 26, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.

MLA Handbook (7^th Edition):

Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course.” 2011. Web. 26 Feb 2020.

Vancouver:

Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2020 Feb 26]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.

Council of Science Editors:

Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to Predict the Outcome of Tuberculosis Treatment Course. [Doctoral Dissertation]. University of Manchester; 2011. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404

Universidade Nova

15. Madsen, Jacob Hastrup. Outlier detection for improved clustering : empirical research for unsupervised data mining.

Degree: 2018, Universidade Nova

URL: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464

► Many clustering algorithms are sensitive to noise disturbing the results when trying to identify and characterize clusters in data. Due to the multidimensional nature of… (more)

Subjects/Keywords: Outlier Detection; Unsupervised Learning; Clustering; Data Mining

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APA (6^th Edition):

Madsen, J. H. (2018). Outlier detection for improved clustering : empirical research for unsupervised data mining. (Thesis). Universidade Nova. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Madsen, Jacob Hastrup. “Outlier detection for improved clustering : empirical research for unsupervised data mining.” 2018. Thesis, Universidade Nova. Accessed February 26, 2020. https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Madsen, Jacob Hastrup. “Outlier detection for improved clustering : empirical research for unsupervised data mining.” 2018. Web. 26 Feb 2020.

Vancouver:

Madsen JH. Outlier detection for improved clustering : empirical research for unsupervised data mining. [Internet] [Thesis]. Universidade Nova; 2018. [cited 2020 Feb 26]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Madsen JH. Outlier detection for improved clustering : empirical research for unsupervised data mining. [Thesis]. Universidade Nova; 2018. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Minnesota

16. Traganitis, Panagiotis. Scalable and Ensemble Learning for Big Data.

Degree: PhD, Electrical/Computer Engineering, 2019, University of Minnesota

URL: http://hdl.handle.net/11299/206358

► The turn of the decade has trademarked society and computing research with a ``data deluge.'' As the number of smart, highly accurate and Internet-capable devices… (more)

▼ The turn of the decade has trademarked society and computing research with a ``data deluge.'' As the number of smart, highly accurate and Internet-capable devices increases, so does the amount of data that is generated and collected. While this sheer amount of data has the potential to enable high quality inference, and mining of information, it introduces numerous challenges in the processing and pattern analysis, since available statistical inference and machine learning approaches do not necessarily scale well with the number of data and their dimensionality. In addition to the challenges related to scalability, data gathered are often noisy, dynamic, contaminated by outliers or corrupted to specifically inhibit the inference task. Moreover, many machine learning approaches have been shown to be susceptible to adversarial attacks. At the same time, the cost of cloud and distributed computing is rapidly declining. Therefore, there is a pressing need for statistical inference and machine learning tools that are robust to attacks and scale with the volume and dimensionality of the data, by harnessing efficiently the available computational resources. This thesis is centered on analytical and algorithmic foundations that aim to enable statistical inference and data analytics from large volumes of high-dimensional data. The vision is to establish a comprehensive framework based on state-of-the-art machine learning, optimization and statistical inference tools to enable truly large-scale inference, which can tap on the available (possibly distributed) computational resources, and be resilient to adversarial attacks. The ultimate goal is to both analytically and numerically demonstrate how valuable insights from signal processing can lead to markedly improved and accelerated learning tools. To this end, the present thesis investigates two main research thrusts: i) Large-scale subspace clustering; and ii) unsupervised ensemble learning. The aforementioned research thrusts introduce novel algorithms that aim to tackle the issues of large-scale learning. The potential of the proposed algorithms is showcased by rigorous theoretical results and extensive numerical tests.

Subjects/Keywords: Big Data; clustering; Ensemble; learning; subspace; unsupervised

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APA (6^th Edition):

Traganitis, P. (2019). Scalable and Ensemble Learning for Big Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206358

Chicago Manual of Style (16^th Edition):

Traganitis, Panagiotis. “Scalable and Ensemble Learning for Big Data.” 2019. Doctoral Dissertation, University of Minnesota. Accessed February 26, 2020. http://hdl.handle.net/11299/206358.

MLA Handbook (7^th Edition):

Traganitis, Panagiotis. “Scalable and Ensemble Learning for Big Data.” 2019. Web. 26 Feb 2020.

Vancouver:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/11299/206358.

Council of Science Editors:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206358

University of Toronto

17. Miasnikof, Pierre. Subgraph Density and Graph Clustering.

Degree: PhD, 2019, University of Toronto

URL: http://hdl.handle.net/1807/97609

► Graph clustering, also often referred to as network community detection, is an unsupervised learning task. It is the process of grouping vertices into sets of… (more)

▼ Graph clustering, also often referred to as network community detection, is an unsupervised learning task. It is the process of grouping vertices into sets of vertices that share common relations. Graph clustering is a pivotal topic in the field of network science. It has applications in various areas such as the analysis of chemical, biological and social networks and any other area where a macroscopic systems point of view is important. Currently, three open problems emerge from the literature: determining if a graph is clusterable before any clustering effort is undertaken, assessing the quality of clusterings identified by an algorithm and the development of fast accurate clustering techniques. In this dissertation, we attempt to answer these three open questions. We use heterogeneity in local densities to determine whether a graph is suited for clustering, if it has a clustered structure to begin with. We also compare global, mean intra-cluster and mean inter-cluster densities to assess the quality and strength of the clusters identified by any clustering algorithm. Finally, as a minor contribution, we examine the possibility of using intra-cluster density maximization as a clustering algorithm. Our clusterability test is shown to be more accurate than techniques proposed in the very recent literature and to rely on fewer assumptions. We demonstrate how heterogeneous densities can be used to identify graphs that display a clustered structure. On the topic of clustering quality assessment, we offer an alternative to the very widely used and problematic modularity quality function. This alternative is consistent with the sets of axioms defining good clustering quality functions, which have been presented by numerous authors. We base our measures on our own definition of clustering quality and accompanying necessary and sufficient conditions. Our approach is shown to be more responsive to connectivity patterns than both modularity and conductance measures. Finally, we also explore intra-cluster density as an objective function to be maximized, for the purpose of clustering. We show how an ensemble optimization meta-heuristic may offer a solution to the clustering problem. Advisors/Committee Members: Lawryshyn, Yuri, Pardalos, Panos M., Chemical Engineering Applied Chemistry.

Subjects/Keywords: Data Science; Graph Clustering; Unsupervised Learning; 0463

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APA (6^th Edition):

Miasnikof, P. (2019). Subgraph Density and Graph Clustering. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/97609

Chicago Manual of Style (16^th Edition):

Miasnikof, Pierre. “Subgraph Density and Graph Clustering.” 2019. Doctoral Dissertation, University of Toronto. Accessed February 26, 2020. http://hdl.handle.net/1807/97609.

MLA Handbook (7^th Edition):

Miasnikof, Pierre. “Subgraph Density and Graph Clustering.” 2019. Web. 26 Feb 2020.

Vancouver:

Miasnikof P. Subgraph Density and Graph Clustering. [Internet] [Doctoral dissertation]. University of Toronto; 2019. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/1807/97609.

Council of Science Editors:

Miasnikof P. Subgraph Density and Graph Clustering. [Doctoral Dissertation]. University of Toronto; 2019. Available from: http://hdl.handle.net/1807/97609

George Mason University

18. Wang, Pu. Nonparametric Bayesian Models for Unsupervised Learning .

Degree: 2011, George Mason University

URL: http://hdl.handle.net/1920/6360

► Unsupervised learning is an important topic in machine learning. In particular, clustering is an unsupervised learning problem that arises in a variety of applications for… (more)

▼ Unsupervised learning is an important topic in machine learning. In particular, clustering is an unsupervised learning problem that arises in a variety of applications for data analysis and mining. Unfortunately, clustering is an ill-posed problem and, as such, a challenging one: no ground-truth that can be used to validate clustering results is available. Two issues arise as a consequence. Various clustering algorithms embed their own bias resulting from di erent optimization criteria. As a result, each algorithm may discover di erent patterns in a given dataset. The second issue concerns the setting of parameters. In clustering, parameter setting controls the characterization of individual clusters, and the total number of clusters in the data. Clustering ensembles have been proposed to address the issue of di erent biases induced by various algorithms. Clustering ensembles combine di erent clustering results, and can provide solutions that are robust against spurious elements in the data. Although clustering ensembles provide a signi cant advance, they do not address satisfactorily the model selection and the parameter tuning problem. Bayesian approaches have been applied to clustering to address the parameter tuning and model selection issues. Bayesian methods provide a principled way to address these problems by assuming prior distributions on model parameters. Prior distributions assign low probabilities to parameter values which are unlikely. Therefore they serve as regularizers for modeling parameters, and can help avoid over- tting. In addition, the marginal likelihood is used by Bayesian approaches as the criterion for model selection. Although Bayesian methods provide a principled way to perform parameter tuning and model selection, the key question \How many clusters?" is still open. This is a fundamental question for model selection. A special kind of Bayesian methods, nonparametric Bayesian approaches, have been proposed to address this important model selection issue. Unlike parametric Bayesian models, for which the number of parameters is nite and xed, nonparametric Bayesian models allow the number of parameters to grow with the number of observations. After observing the data, nonparametric Bayesian models t the data with nite dimensional parameters. An additional issue with clustering is high dimensionality. High-dimensional data pose a di cult challenge to the clustering process. A common scenario with high-dimensional data is that clusters may exist in di erent subspaces comprised of di erent combinations of features (dimensions). In other words, data points in a cluster may be similar to each other along a subset of dimensions, but not in all dimensions. People have proposed subspace clustering techniques, a.k.a. co-clustering or bi-clustering, to address the dimensionality issue (here, I use the term co-clustering). Like clustering, also co-clustering su ers from the ill-posed nature and the lack of ground-truth to validate the results. Although attempts… Advisors/Committee Members: Domeniconi, Carlotta (advisor).

Subjects/Keywords: Unsupervised Learning; Clustering; Bayesian Nonparametrics; Clustering Ensembles

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APA (6^th Edition):

Wang, P. (2011). Nonparametric Bayesian Models for Unsupervised Learning . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/6360

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wang, Pu. “Nonparametric Bayesian Models for Unsupervised Learning .” 2011. Thesis, George Mason University. Accessed February 26, 2020. http://hdl.handle.net/1920/6360.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wang, Pu. “Nonparametric Bayesian Models for Unsupervised Learning .” 2011. Web. 26 Feb 2020.

Vancouver:

Wang P. Nonparametric Bayesian Models for Unsupervised Learning . [Internet] [Thesis]. George Mason University; 2011. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/1920/6360.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang P. Nonparametric Bayesian Models for Unsupervised Learning . [Thesis]. George Mason University; 2011. Available from: http://hdl.handle.net/1920/6360

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of California – Merced

19. Vladymyrov, Maksym. Large-Scale Methods for Nonlinear Manifold Learning.

Degree: Electrical Engineering and Computer Science, 2014, University of California – Merced

URL: http://www.escholarship.org/uc/item/9hj5v8z2

► High-dimensional data representation is an important problem in many different areas of science. Nowadays, it is becoming crucial to interpret the data of varying dimensionality… (more)

Subjects/Keywords: Computer science; dimensionality reduction; machine learning; manifold learning; unsupervised learning

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APA (6^th Edition):

Vladymyrov, M. (2014). Large-Scale Methods for Nonlinear Manifold Learning. (Thesis). University of California – Merced. Retrieved from http://www.escholarship.org/uc/item/9hj5v8z2

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vladymyrov, Maksym. “Large-Scale Methods for Nonlinear Manifold Learning.” 2014. Thesis, University of California – Merced. Accessed February 26, 2020. http://www.escholarship.org/uc/item/9hj5v8z2.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vladymyrov, Maksym. “Large-Scale Methods for Nonlinear Manifold Learning.” 2014. Web. 26 Feb 2020.

Vancouver:

Vladymyrov M. Large-Scale Methods for Nonlinear Manifold Learning. [Internet] [Thesis]. University of California – Merced; 2014. [cited 2020 Feb 26]. Available from: http://www.escholarship.org/uc/item/9hj5v8z2.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vladymyrov M. Large-Scale Methods for Nonlinear Manifold Learning. [Thesis]. University of California – Merced; 2014. Available from: http://www.escholarship.org/uc/item/9hj5v8z2

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Linköping University

20. Alirezaie, Marjan. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.

Degree: Computer and Information Science, 2011, Linköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

► The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem… (more)

Subjects/Keywords: Machine Learning; Supervised Learning; Unsupervised Learning; Computer Sciences; Datavetenskap (datalogi)

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APA (6^th Edition):

Alirezaie, M. (2011). Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Thesis, Linköping University. Accessed February 26, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Web. 26 Feb 2020.

Vancouver:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Internet] [Thesis]. Linköping University; 2011. [cited 2020 Feb 26]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Thesis]. Linköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo

21. Jaini, Priyank. Likelihood-based Density Estimation using Deep Architectures.

Degree: 2019, University of Waterloo

URL: http://hdl.handle.net/10012/15356

► Multivariate density estimation is a central problem in unsupervised machine learning that has been studied immensely in both statistics and machine learning. Several methods have… (more)

▼ Multivariate density estimation is a central problem in unsupervised machine learning that has been studied immensely in both statistics and machine learning. Several methods have thus been proposed for density estimation including classical techniques like histograms, kernel density estimation methods, mixture models, and more recently neural density estimation that leverages the recent advances in deep learning and neural networks to tractably represent a density function. In today's age, when large amounts of data are being generated in almost every field, it is of paramount importance to develop density estimation methods that are cheap both computationally and in memory cost. The main contribution of this thesis is in providing a principled study of parametric density estimation methods using mixture models and triangular maps for neural density estimation. The first part of the thesis focuses on the compact representation of mixture models using deep architectures like latent tree models, hidden Markov models, tensorial mixture models, hierarchical tensor formats and sum-product networks. It provides a unifying view of possible representations of mixture models using such deep architectures. The unifying view allows us to prove exponential separation between deep mixture models and mixture models represented using shallow architectures, demonstrating the benefits of depth in their representation. In a surprising result thereafter, we prove that a deep mixture model can be approximated using the conditional gradient algorithm by a shallow architecture of polynomial size w.r.t. the inverse of the approximation accuracy. Next, we address the more practical problem of density estimation of mixture models for streaming data by proposing an online Bayesian Moment Matching algorithm for Gaussian mixture models that can be distributed over several processors for fast computation. Exact Bayesian learning of mixture models is intractable because the number of terms in the posterior grows exponentially w.r.t. to the number of observations. We circumvent this problem by projecting the exact posterior on to a simple family of densities by matching a set of sufficient moments. Subsequently, we extend this algorithm for sequential data modeling using transfer learning by learning a hidden Markov model over the observations with Gaussian mixtures. We apply this algorithm on three diverse applications of activity recognition based on smartphone sensors, sleep stage classification for predicting neurological disorders using electroencephalography data and network size prediction for telecommunication networks. In the second part, we focus on neural density estimation methods where we provide a unified framework for estimating densities using monotone and bijective triangular maps represented using deep neural networks. Using this unified framework we study the limitations and representation power of recent flow based and autoregressive methods. Based on this framework, we subsequently propose a novel Sum-of-Squares…

Subjects/Keywords: machine learning; unsupervised learning; deep learning; probabilitic graphical models

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APA (6^th Edition):

Jaini, P. (2019). Likelihood-based Density Estimation using Deep Architectures. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15356

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Jaini, Priyank. “Likelihood-based Density Estimation using Deep Architectures.” 2019. Thesis, University of Waterloo. Accessed February 26, 2020. http://hdl.handle.net/10012/15356.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Jaini, Priyank. “Likelihood-based Density Estimation using Deep Architectures.” 2019. Web. 26 Feb 2020.

Vancouver:

Jaini P. Likelihood-based Density Estimation using Deep Architectures. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/10012/15356.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jaini P. Likelihood-based Density Estimation using Deep Architectures. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/15356

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

22. STUART, CHRISTOFFER. Applying Machine Learning to Identify Maintenance Level for Software Releases .

Degree: Chalmers tekniska högskola / Institutionen för data och informationsvetenskap, 2020, Chalmers University of Technology

URL: http://hdl.handle.net/20.500.12380/300701

► Maintenance is the single largest cost in software development. Therefore it is important to understand what causes maintenance, and if it can be predicted. Many… (more)

Subjects/Keywords: Machine learning; supervised learning; unsupervised learning; defect prediction; cumulative failure prediction

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APA (6^th Edition):

STUART, C. (2020). Applying Machine Learning to Identify Maintenance Level for Software Releases . (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/300701

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

STUART, CHRISTOFFER. “Applying Machine Learning to Identify Maintenance Level for Software Releases .” 2020. Thesis, Chalmers University of Technology. Accessed February 26, 2020. http://hdl.handle.net/20.500.12380/300701.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

STUART, CHRISTOFFER. “Applying Machine Learning to Identify Maintenance Level for Software Releases .” 2020. Web. 26 Feb 2020.

Vancouver:

STUART C. Applying Machine Learning to Identify Maintenance Level for Software Releases . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/20.500.12380/300701.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

STUART C. Applying Machine Learning to Identify Maintenance Level for Software Releases . [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/300701

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Manchester

23. Parkinson, Jon Charles. Representation learning with a temporally coherent mixed-representation.

Degree: 2017, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982

►

Guiding a representation towards capturing temporally coherent aspects present invideo improves object identity encoding. Existing models apply temporal coherenceuniformly over all features based on the… (more)

▼

Guiding a representation towards capturing temporally coherent aspects present invideo improves object identity encoding. Existing models apply temporal coherenceuniformly over all features based on the assumption that optimal encoding of objectidentity only requires temporally stable components. We test the validity of this assumptionby exploring the effects of applying a mixture of temporally coherent invariantfeatures, alongside variable features, in a single ‘mixed’ representation. Applyingtemporal coherence to different proportions of the available features, we evaluate arange of models on a supervised object classification task. This series of experimentswas tested on three video datasets, each with a different complexity of object shape andmotion. We also investigated whether a mixed-representation improves the capture ofinformation components associated with object position, alongside object identity, ina single representation. Tests were initially applied using a single layer autoencoderas a test bed, followed by subsequent tests investigating whether similar behaviouroccurred in the more abstract features learned by a deep network. A representationapplying temporal coherence in some fashion produced the best results in all tests,on both single layered and deep networks. The majority of tests favoured a mixed representation,especially in cases where the quantity of labelled data available to thesupervised task was plentiful. This work is the first time a mixed-representation hasbeen investigated, and demonstrates its use as a method for representation learning.

Guiding a representation towards capturing temporally coherent aspects present in video improves object identity encoding.Existing models apply temporal coherence uniformly over all features based on the assumption that optimal encoding of object identity only requires temporally stable components.We test the validity of this assumption by exploring the effects of applying a mixture of temporally coherent invariant features, alongside variable features, in a single `mixed' representation. Applying temporal coherence to different proportions of the available features, we evaluate a range of models on a supervised object classification task.This series of experiments was tested on three video datasets, each with a different complexity of object shape and motion. We also investigated whether a mixed-representation improves the capture of information components associated with object position, alongside object identity, in a single representation. Tests were initially applied using a single layer autoencoder as a test bed, followed by subsequent tests investigating whether similar behaviour occurred in the more abstract features learned by a deep network.A representation applying temporal coherence in some fashion produced the best results in all tests, on both single layered and deep networks.The majority of tests favoured a mixed-representation, especially in cases where the quantity of labelled data available to the supervised task was…

Advisors/Committee Members: SHAPIRO, JONATHAN JL, Chen, Ke, Shapiro, Jonathan.

Subjects/Keywords: Representation learning; Neural Networks; Temporal coherence; Autoencoders; Unsupervised learning; Computer vision

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Parkinson, J. C. (2017). Representation learning with a temporally coherent mixed-representation. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982

Chicago Manual of Style (16^th Edition):

Parkinson, Jon Charles. “Representation learning with a temporally coherent mixed-representation.” 2017. Doctoral Dissertation, University of Manchester. Accessed February 26, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982.

MLA Handbook (7^th Edition):

Parkinson, Jon Charles. “Representation learning with a temporally coherent mixed-representation.” 2017. Web. 26 Feb 2020.

Vancouver:

Parkinson JC. Representation learning with a temporally coherent mixed-representation. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2020 Feb 26]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982.

Council of Science Editors:

Parkinson JC. Representation learning with a temporally coherent mixed-representation. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:308982

Cornell University

24. Sener, Ozan. Learning From Large-Scale Visual Data For Robots .

Degree: 2016, Cornell University

URL: http://hdl.handle.net/1813/45306

Subjects/Keywords: Robotics; Unsupervised Learning; Deep Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sener, O. (2016). Learning From Large-Scale Visual Data For Robots . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/45306

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sener, Ozan. “Learning From Large-Scale Visual Data For Robots .” 2016. Thesis, Cornell University. Accessed February 26, 2020. http://hdl.handle.net/1813/45306.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sener, Ozan. “Learning From Large-Scale Visual Data For Robots .” 2016. Web. 26 Feb 2020.

Vancouver:

Sener O. Learning From Large-Scale Visual Data For Robots . [Internet] [Thesis]. Cornell University; 2016. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/1813/45306.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sener O. Learning From Large-Scale Visual Data For Robots . [Thesis]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/45306

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Tampere University

25. Shafiq, Omer. Anomaly Detection In Blockchain .

Degree: 2019, Tampere University

URL: https://trepo.tuni.fi/handle/10024/118552

► Anomaly detection has been a well-studied area for a long time. Its applications in the financial sector have aided in identifying suspicious activities of hackers.… (more)

Subjects/Keywords: blockchain; bitcoin; anomaly detection; unsupervised learning; deep learning; master's thesis

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APA (6^th Edition):

Shafiq, O. (2019). Anomaly Detection In Blockchain . (Masters Thesis). Tampere University. Retrieved from https://trepo.tuni.fi/handle/10024/118552

Chicago Manual of Style (16^th Edition):

Shafiq, Omer. “Anomaly Detection In Blockchain .” 2019. Masters Thesis, Tampere University. Accessed February 26, 2020. https://trepo.tuni.fi/handle/10024/118552.

MLA Handbook (7^th Edition):

Shafiq, Omer. “Anomaly Detection In Blockchain .” 2019. Web. 26 Feb 2020.

Vancouver:

Shafiq O. Anomaly Detection In Blockchain . [Internet] [Masters thesis]. Tampere University; 2019. [cited 2020 Feb 26]. Available from: https://trepo.tuni.fi/handle/10024/118552.

Council of Science Editors:

Shafiq O. Anomaly Detection In Blockchain . [Masters Thesis]. Tampere University; 2019. Available from: https://trepo.tuni.fi/handle/10024/118552

University of Edinburgh

26. Heess, Nicolas Manfred Otto. Learning generative models of mid-level structure in natural images.

Degree: PhD, 2012, University of Edinburgh

URL: http://hdl.handle.net/1842/5866

► Natural images arise from complicated processes involving many factors of variation. They reflect the wealth of shapes and appearances of objects in our three-dimensional world,… (more)

▼ Natural images arise from complicated processes involving many factors of variation. They reflect the wealth of shapes and appearances of objects in our three-dimensional world, but they are also affected by factors such as distortions due to perspective, occlusions, and illumination, giving rise to structure with regularities at many different levels. Prior knowledge about these regularities and suitable representations that allow efficient reasoning about the properties of a visual scene are important for many image processing and computer vision tasks. This thesis focuses on models of image structure at intermediate levels of complexity as required, for instance, for image inpainting or segmentation. It aims at developing generative, probabilistic models of this kind of structure, and, in particular, at devising strategies for learning such models in a largely unsupervised manner from data. One hallmark of natural images is that they can often be decomposed into regions with very different visual characteristics. The main approach of this thesis is therefore to represent images in terms of regions that are characterized by their shapes and appearances, and an image is then composed from many such regions. We explore approaches to learn about the appearance of regions, to learn about region shapes, and ways to combine several regions to form a full image. To achieve this goal, we make use of some ideas for unsupervised learning developed in the literature on models of low-level image structure and in the “deep learning” literature. These models are used as building blocks of more structured model formulations that incorporate additional prior knowledge of how images are formed. The thesis makes the following contributions: Firstly, we investigate a popular, MRF based prior of natural image structure, the Field-of Experts, with respect to its ability to model image textures, and propose an extended formulation that is considerably more successful at this task. This formulation gives rise to a fully parametric, translation-invariant probabilistic generative model of image textures. We illustrate how this model can be used as a component of a more comprehensive model of images comprising multiple textured regions. Secondly, we develop a model of region shape. This work is an extension of the “Masked Restricted Boltzmann Machine” proposed by Le Roux et al. (2011) and it allows explicit reasoning about the independent shapes and relative depths of occluding objects. We develop an inference and unsupervised learning scheme and demonstrate how this shape model, in combination with the masked RBM gives rise to a good model of natural image patches. Finally, we demonstrate how this model of region shape can be extended to model shapes in large images. The result is a generative model of large images which are formed by composition from many small, partially overlapping and occluding objects.

Subjects/Keywords: 006.3; machine learning; unsupervised learning; generative models; computer vision

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Heess, N. M. O. (2012). Learning generative models of mid-level structure in natural images. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/5866

Chicago Manual of Style (16^th Edition):

Heess, Nicolas Manfred Otto. “Learning generative models of mid-level structure in natural images.” 2012. Doctoral Dissertation, University of Edinburgh. Accessed February 26, 2020. http://hdl.handle.net/1842/5866.

MLA Handbook (7^th Edition):

Heess, Nicolas Manfred Otto. “Learning generative models of mid-level structure in natural images.” 2012. Web. 26 Feb 2020.

Vancouver:

Heess NMO. Learning generative models of mid-level structure in natural images. [Internet] [Doctoral dissertation]. University of Edinburgh; 2012. [cited 2020 Feb 26]. Available from: http://hdl.handle.net/1842/5866.

Council of Science Editors:

Heess NMO. Learning generative models of mid-level structure in natural images. [Doctoral Dissertation]. University of Edinburgh; 2012. Available from: http://hdl.handle.net/1842/5866

Penn State University

27. Garg, Mayank. BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING.

Degree: 2017, Penn State University

URL: https://etda.libraries.psu.edu/catalog/14213mxg1042

► This thesis studies the battery state of charge (SOC) estimation and battery anomaly detection using machine learning technique. Classically, battery SOC is estimated using a… (more)

Subjects/Keywords: Battery SOC estimation; Battery anomaly detection; Machine learning; Supervised Learning; Unsupervised Learning

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APA (6^th Edition):

Garg, M. (2017). BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING. (Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/14213mxg1042

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Garg, Mayank. “BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING.” 2017. Thesis, Penn State University. Accessed February 26, 2020. https://etda.libraries.psu.edu/catalog/14213mxg1042.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Garg, Mayank. “BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING.” 2017. Web. 26 Feb 2020.

Vancouver:

Garg M. BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING. [Internet] [Thesis]. Penn State University; 2017. [cited 2020 Feb 26]. Available from: https://etda.libraries.psu.edu/catalog/14213mxg1042.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Garg M. BATTERY STATE ESTIMATION AND ANOMALY DETECTION USING MACHINE LEARNING. [Thesis]. Penn State University; 2017. Available from: https://etda.libraries.psu.edu/catalog/14213mxg1042

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

28. Bui, Thang Duc. Efficient deterministic approximate Bayesian inference for Gaussian process models.

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600

► Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian… (more)

▼ Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian processes in the large-data regime and in hierarchical probabilistic models is severely limited by analytic and computational intractabilities. It is, therefore, important to develop practical approximate inference and learning algorithms that can address these challenges. To this end, this dissertation provides a comprehensive and unifying perspective of pseudo-point based deterministic approximate Bayesian learning for a wide variety of Gaussian process models, which connects previously disparate literature, greatly extends them and allows new state-of-the-art approximations to emerge. We start by building a posterior approximation framework based on Power-Expectation Propagation for Gaussian process regression and classification. This framework relies on a structured approximate Gaussian process posterior based on a small number of pseudo-points, which is judiciously chosen to summarise the actual data and enable tractable and efficient inference and hyperparameter learning. Many existing sparse approximations are recovered as special cases of this framework, and can now be understood as performing approximate posterior inference using a common approximate posterior. Critically, extensive empirical evidence suggests that new approximation methods arisen from this unifying perspective outperform existing approaches in many real-world regression and classification tasks. We explore the extensions of this framework to Gaussian process state space models, Gaussian process latent variable models and deep Gaussian processes, which also unify many recently developed approximation schemes for these models. Several mean-field and structured approximate posterior families for the hidden variables in these models are studied. We also discuss several methods for approximate uncertainty propagation in recurrent and deep architectures based on Gaussian projection, linearisation, and simple Monte Carlo. The benefit of the unified inference and learning frameworks for these models are illustrated in a variety of real-world state-space modelling and regression tasks.

Subjects/Keywords: machine learning; Gaussian process; approximate inference; Bayesian statistics; supervised learning; unsupervised learning

1 more image…

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APA (6^th Edition):

Bui, T. D. (2018). Efficient deterministic approximate Bayesian inference for Gaussian process models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600

Chicago Manual of Style (16^th Edition):

Bui, Thang Duc. “Efficient deterministic approximate Bayesian inference for Gaussian process models.” 2018. Doctoral Dissertation, University of Cambridge. Accessed February 26, 2020. https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600.

MLA Handbook (7^th Edition):

Bui, Thang Duc. “Efficient deterministic approximate Bayesian inference for Gaussian process models.” 2018. Web. 26 Feb 2020.

Vancouver:

Bui TD. Efficient deterministic approximate Bayesian inference for Gaussian process models. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2020 Feb 26]. Available from: https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600.

Council of Science Editors:

Bui TD. Efficient deterministic approximate Bayesian inference for Gaussian process models. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/273833 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744600

University of Cambridge

29. Bui, Thang Duc. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models.

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/273833

► Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian… (more)

▼ Gaussian processes are powerful nonparametric distributions over continuous functions that have become a standard tool in modern probabilistic machine learning. However, the applicability of Gaussian processes in the large-data regime and in hierarchical probabilistic models is severely limited by analytic and computational intractabilities. It is, therefore, important to develop practical approximate inference and learning algorithms that can address these challenges. To this end, this dissertation provides a comprehensive and unifying perspective of pseudo-point based deterministic approximate Bayesian learning for a wide variety of Gaussian process models, which connects previously disparate literature, greatly extends them and allows new state-of-the-art approximations to emerge. We start by building a posterior approximation framework based on Power-Expectation Propagation for Gaussian process regression and classification. This framework relies on a structured approximate Gaussian process posterior based on a small number of pseudo-points, which is judiciously chosen to summarise the actual data and enable tractable and efficient inference and hyperparameter learning. Many existing sparse approximations are recovered as special cases of this framework, and can now be understood as performing approximate posterior inference using a common approximate posterior. Critically, extensive empirical evidence suggests that new approximation methods arisen from this unifying perspective outperform existing approaches in many real-world regression and classification tasks. We explore the extensions of this framework to Gaussian process state space models, Gaussian process latent variable models and deep Gaussian processes, which also unify many recently developed approximation schemes for these models. Several mean-field and structured approximate posterior families for the hidden variables in these models are studied. We also discuss several methods for approximate uncertainty propagation in recurrent and deep architectures based on Gaussian projection, linearisation, and simple Monte Carlo. The benefit of the unified inference and learning frameworks for these models are illustrated in a variety of real-world state-space modelling and regression tasks.

Subjects/Keywords: machine learning; Gaussian process; approximate inference; Bayesian statistics; supervised learning; unsupervised learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Bui, T. D. (2018). Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/273833

Chicago Manual of Style (16^th Edition):

Bui, Thang Duc. “Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models.” 2018. Doctoral Dissertation, University of Cambridge. Accessed February 26, 2020. https://www.repository.cam.ac.uk/handle/1810/273833.

MLA Handbook (7^th Edition):

Bui, Thang Duc. “Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models.” 2018. Web. 26 Feb 2020.

Vancouver:

Bui TD. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2020 Feb 26]. Available from: https://www.repository.cam.ac.uk/handle/1810/273833.

Council of Science Editors:

Bui TD. Efficient Deterministic Approximate Bayesian Inference for Gaussian Process models. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/273833

30. Li, Yingzhen. Approximate inference : new visions.

Degree: PhD, 2018, University of Cambridge

URL: https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977

► Nowadays machine learning (especially deep learning) techniques are being incorporated to many intelligent systems affecting the quality of human life. The ultimate purpose of these… (more)

▼ Nowadays machine learning (especially deep learning) techniques are being incorporated to many intelligent systems affecting the quality of human life. The ultimate purpose of these systems is to perform automated decision making, and in order to achieve this, predictive systems need to return estimates of their confidence. Powered by the rules of probability, Bayesian inference is the gold standard method to perform coherent reasoning under uncertainty. It is generally believed that intelligent systems following the Bayesian approach can better incorporate uncertainty information for reliable decision making, and be less vulnerable to attacks such as data poisoning. Critically, the success of Bayesian methods in practice, including the recent resurgence of Bayesian deep learning, relies on fast and accurate approximate Bayesian inference applied to probabilistic models. These approximate inference methods perform (approximate) Bayesian reasoning at a relatively low cost in terms of time and memory, thus allowing the principles of Bayesian modelling to be applied to many practical settings. However, more work needs to be done to scale approximate Bayesian inference methods to big systems such as deep neural networks and large-scale dataset such as ImageNet. In this thesis we develop new algorithms towards addressing the open challenges in approximate inference. In the first part of the thesis we develop two new approximate inference algorithms, by drawing inspiration from the well known expectation propagation and message passing algorithms. Both approaches provide a unifying view of existing variational methods from different algorithmic perspectives. We also demonstrate that they lead to better calibrated inference results for complex models such as neural network classifiers and deep generative models, and scale to large datasets containing hundreds of thousands of data-points. In the second theme of the thesis we propose a new research direction for approximate inference: developing algorithms for fitting posterior approximations of arbitrary form, by rethinking the fundamental principles of Bayesian computation and the necessity of algorithmic constraints in traditional inference schemes. We specify four algorithmic options for the development of such new generation approximate inference methods, with one of them further investigated and applied to Bayesian deep learning tasks.

Subjects/Keywords: Bayesian statistics; Machine learning; Deep learning; Monte Carlo; Approximate inference; Neural networks; unsupervised learning

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Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Li, Y. (2018). Approximate inference : new visions. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977

Chicago Manual of Style (16^th Edition):

Li, Yingzhen. “Approximate inference : new visions.” 2018. Doctoral Dissertation, University of Cambridge. Accessed February 26, 2020. https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977.

MLA Handbook (7^th Edition):

Li, Yingzhen. “Approximate inference : new visions.” 2018. Web. 26 Feb 2020.

Vancouver:

Li Y. Approximate inference : new visions. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2020 Feb 26]. Available from: https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977.

Council of Science Editors:

Li Y. Approximate inference : new visions. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/277549 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744977

Open Access Theses and Dissertations