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You searched for subject:(unsupervised learning). Showing records 1 – 30 of 361 total matches.

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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 <i>n</i> textes, le but est… (more)

Subjects/Keywords: unsupervised learning

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

APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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-....)

 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)

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 (6th 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 (16th 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 (7th 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

Subjects/Keywords: unsupervised learning

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

APA (6th 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 (16th 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 (7th 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

Subjects/Keywords: Unsupervised learning

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

APA (6th 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 (16th 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 (7th 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

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

APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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)

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

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

APA (6th 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 (16th 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 (7th 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

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

APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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

APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

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)

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

  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 (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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)

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

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APA (6th 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 (16th 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 (7th 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

Subjects/Keywords: Robotics; Unsupervised Learning; Deep Learning

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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)

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

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

 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)

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

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

APA (6th 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 (16th 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 (7th 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

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