Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Unsupervised learning). Showing records 1 – 30 of 455 total matches.

[1] [2] [3] [4] [5] … [16]

Search Limiters

Last 2 Years | English Only

Degrees

Levels

Languages

Country

▼ Search Limiters

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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 November 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 Nov 2020.

Vancouver:

Kocher M. Text clustering with styles. [Internet] [Thesis]. Université de Neuchâtel; 2017. [cited 2020 Nov 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 2020.

Vancouver:

Butler-Yeoman T. Learning to Disentangle the Complex Causes of Data. [Internet] [Masters thesis]. Victoria University of Wellington; 2017. [cited 2020 Nov 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 November 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 Nov 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 Nov 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 November 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 Nov 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 Nov 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 November 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 Nov 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 Nov 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: Master of Applied Science, School of Engineering, 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809

Chicago Manual of Style (16th Edition):

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

MLA Handbook (7th Edition):

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

Vancouver:

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

Council of Science Editors:

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


University of Toronto

7. Makhzani, Alireza. Unsupervised Representation Learning with Autoencoders.

Degree: PhD, 2018, University of Toronto

 Despite the recent progress in machine learning and deep learning, unsupervised learning still remains a largely unsolved problem. It is widely recognized that unsupervised learning(more)

Subjects/Keywords: Deep Learning; Machine Learning; Unsupervised Learning; 0984

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Makhzani, A. (2018). Unsupervised Representation Learning with Autoencoders. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/89800

Chicago Manual of Style (16th Edition):

Makhzani, Alireza. “Unsupervised Representation Learning with Autoencoders.” 2018. Doctoral Dissertation, University of Toronto. Accessed November 26, 2020. http://hdl.handle.net/1807/89800.

MLA Handbook (7th Edition):

Makhzani, Alireza. “Unsupervised Representation Learning with Autoencoders.” 2018. Web. 26 Nov 2020.

Vancouver:

Makhzani A. Unsupervised Representation Learning with Autoencoders. [Internet] [Doctoral dissertation]. University of Toronto; 2018. [cited 2020 Nov 26]. Available from: http://hdl.handle.net/1807/89800.

Council of Science Editors:

Makhzani A. Unsupervised Representation Learning with Autoencoders. [Doctoral Dissertation]. University of Toronto; 2018. Available from: http://hdl.handle.net/1807/89800


University of Alberta

8. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 26, 2020. https://era.library.ualberta.ca/files/gq67jt70x.

MLA Handbook (7th Edition):

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

Vancouver:

White M. Regularized factor models. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2020 Nov 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

9. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 2020.

Vancouver:

Bod G. Self-taught learning: Implementation using MATLAB . [Internet] [Thesis]. University of Debrecen; 2014. [cited 2020 Nov 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 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 November 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 Nov 2020.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2020 Nov 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

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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 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 Nov 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 New South Wales

12. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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 November 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 Nov 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 Nov 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


Georgia Tech

13. Haresamudram, Harish. The role of representations in human activity recognition.

Degree: MS, Electrical and Computer Engineering, 2019, Georgia Tech

 We investigate the role of representations in sensor based human activity recognition (HAR). In particular, we develop convolutional and recurrent autoencoder architectures for feature learning(more)

Subjects/Keywords: Unsupervised learning; Human activity recognition; Autoencoder models

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Haresamudram, H. (2019). The role of representations in human activity recognition. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62706

Chicago Manual of Style (16th Edition):

Haresamudram, Harish. “The role of representations in human activity recognition.” 2019. Masters Thesis, Georgia Tech. Accessed November 26, 2020. http://hdl.handle.net/1853/62706.

MLA Handbook (7th Edition):

Haresamudram, Harish. “The role of representations in human activity recognition.” 2019. Web. 26 Nov 2020.

Vancouver:

Haresamudram H. The role of representations in human activity recognition. [Internet] [Masters thesis]. Georgia Tech; 2019. [cited 2020 Nov 26]. Available from: http://hdl.handle.net/1853/62706.

Council of Science Editors:

Haresamudram H. The role of representations in human activity recognition. [Masters Thesis]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62706


Universidade Nova

14. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 2020.

Vancouver:

Madsen JH. Outlier detection for improved clustering : empirical research for unsupervised data mining. [Internet] [Thesis]. Universidade Nova; 2018. [cited 2020 Nov 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 Manchester

15. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 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 Nov 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


George Mason University

16. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 2020.

Vancouver:

Wang P. Nonparametric Bayesian Models for Unsupervised Learning . [Internet] [Thesis]. George Mason University; 2011. [cited 2020 Nov 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

17. ALAKKARI, SALAHEDDIN. Modelling Large-scale Datasets Using Principal Component Analysis.

Degree: School of Computer Science & Statistics. Discipline of Computer Science, 2020, Trinity College Dublin

 Principal Component Analysis (PCA) is one of the most well-known unsupervised learning techniques used for dimensionality reduction and feature extraction. The main task of PCA… (more)

Subjects/Keywords: dimensionality reduction; unsupervised machine learning; data analysis

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

ALAKKARI, S. (2020). Modelling Large-scale Datasets Using Principal Component Analysis. (Thesis). Trinity College Dublin. Retrieved from http://hdl.handle.net/2262/92525

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):

ALAKKARI, SALAHEDDIN. “Modelling Large-scale Datasets Using Principal Component Analysis.” 2020. Thesis, Trinity College Dublin. Accessed November 26, 2020. http://hdl.handle.net/2262/92525.

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

MLA Handbook (7th Edition):

ALAKKARI, SALAHEDDIN. “Modelling Large-scale Datasets Using Principal Component Analysis.” 2020. Web. 26 Nov 2020.

Vancouver:

ALAKKARI S. Modelling Large-scale Datasets Using Principal Component Analysis. [Internet] [Thesis]. Trinity College Dublin; 2020. [cited 2020 Nov 26]. Available from: http://hdl.handle.net/2262/92525.

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

Council of Science Editors:

ALAKKARI S. Modelling Large-scale Datasets Using Principal Component Analysis. [Thesis]. Trinity College Dublin; 2020. Available from: http://hdl.handle.net/2262/92525

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


University of Toronto

18. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 26, 2020. http://hdl.handle.net/1807/97609.

MLA Handbook (7th Edition):

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

Vancouver:

Miasnikof P. Subgraph Density and Graph Clustering. [Internet] [Doctoral dissertation]. University of Toronto; 2019. [cited 2020 Nov 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


University of Minnesota

19. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 26, 2020. http://hdl.handle.net/11299/206358.

MLA Handbook (7th Edition):

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

Vancouver:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2020 Nov 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


Texas Tech University

20. Hill, Jason E. Application of Information Theoretic Unsupervised Learning to Medical Image Analysis.

Degree: 2013, Texas Tech University

 Automated segmentation of medical images is a challenging problem. The number of segments in a medical image may be unknown a priori, due to the… (more)

Subjects/Keywords: Unsupervised learning; Medical images; Spectral clustering.

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Hill, J. E. (2013). Application of Information Theoretic Unsupervised Learning to Medical Image Analysis. (Thesis). Texas Tech University. Retrieved from http://hdl.handle.net/2346/48865

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):

Hill, Jason E. “Application of Information Theoretic Unsupervised Learning to Medical Image Analysis.” 2013. Thesis, Texas Tech University. Accessed November 26, 2020. http://hdl.handle.net/2346/48865.

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

MLA Handbook (7th Edition):

Hill, Jason E. “Application of Information Theoretic Unsupervised Learning to Medical Image Analysis.” 2013. Web. 26 Nov 2020.

Vancouver:

Hill JE. Application of Information Theoretic Unsupervised Learning to Medical Image Analysis. [Internet] [Thesis]. Texas Tech University; 2013. [cited 2020 Nov 26]. Available from: http://hdl.handle.net/2346/48865.

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

Council of Science Editors:

Hill JE. Application of Information Theoretic Unsupervised Learning to Medical Image Analysis. [Thesis]. Texas Tech University; 2013. Available from: http://hdl.handle.net/2346/48865

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


University of Southern California

21. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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/3793

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 November 26, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3793.

MLA Handbook (7th Edition):

Deutsch, Shay. “Learning the geometric structure of high dimensional data using the Tensor Voting Graph.” 2017. Web. 26 Nov 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 Nov 26]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/640839/rec/3793.

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/3793

22. Perundurai Rajasekaran, Siddharthan. Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks.

Degree: MS, 2017, Worcester Polytechnic Institute

 "This thesis focuses on two key problems in reinforcement learning: How to design reward functions to obtain intended behaviors in autonomous systems using the learning-based… (more)

Subjects/Keywords: Learning with uncertainty; Unsupervised learning; Reinforcement Learning; Inverse Reinforcement Learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Perundurai Rajasekaran, S. (2017). Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks. (Thesis). Worcester Polytechnic Institute. Retrieved from etd-083017-144531 ; https://digitalcommons.wpi.edu/etd-theses/1205

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):

Perundurai Rajasekaran, Siddharthan. “Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks.” 2017. Thesis, Worcester Polytechnic Institute. Accessed November 26, 2020. etd-083017-144531 ; https://digitalcommons.wpi.edu/etd-theses/1205.

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

MLA Handbook (7th Edition):

Perundurai Rajasekaran, Siddharthan. “Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks.” 2017. Web. 26 Nov 2020.

Vancouver:

Perundurai Rajasekaran S. Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks. [Internet] [Thesis]. Worcester Polytechnic Institute; 2017. [cited 2020 Nov 26]. Available from: etd-083017-144531 ; https://digitalcommons.wpi.edu/etd-theses/1205.

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

Council of Science Editors:

Perundurai Rajasekaran S. Nonparametric Inverse Reinforcement Learning and Approximate Optimal Control with Temporal Logic Tasks. [Thesis]. Worcester Polytechnic Institute; 2017. Available from: etd-083017-144531 ; https://digitalcommons.wpi.edu/etd-theses/1205

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


University of California – Merced

23. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 2020.

Vancouver:

Vladymyrov M. Large-Scale Methods for Nonlinear Manifold Learning. [Internet] [Thesis]. University of California – Merced; 2014. [cited 2020 Nov 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


Penn State University

24. Shen, Sida. Sepsis Data Analytics.

Degree: 2020, Penn State University

 Sepsis is a potentially life-threatening condition caused by the body’s response to infection. Body releases chemicals into the blood stream to fight infection. However, sepsis… (more)

Subjects/Keywords: Machine learning; Deep learning; Sepsis; Healthcare; Unsupervised Learning; Data Mining

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shen, S. (2020). Sepsis Data Analytics. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/17780sps5425

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):

Shen, Sida. “Sepsis Data Analytics.” 2020. Thesis, Penn State University. Accessed November 26, 2020. https://submit-etda.libraries.psu.edu/catalog/17780sps5425.

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

MLA Handbook (7th Edition):

Shen, Sida. “Sepsis Data Analytics.” 2020. Web. 26 Nov 2020.

Vancouver:

Shen S. Sepsis Data Analytics. [Internet] [Thesis]. Penn State University; 2020. [cited 2020 Nov 26]. Available from: https://submit-etda.libraries.psu.edu/catalog/17780sps5425.

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

Council of Science Editors:

Shen S. Sepsis Data Analytics. [Thesis]. Penn State University; 2020. Available from: https://submit-etda.libraries.psu.edu/catalog/17780sps5425

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


University of Waterloo

25. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 2020.

Vancouver:

Jaini P. Likelihood-based Density Estimation using Deep Architectures. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2020 Nov 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


Linköping University

26. 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)

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 2020.

Vancouver:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Internet] [Thesis]. Linköping University; 2011. [cited 2020 Nov 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 Toronto

27. Srivastava, Nitish. Deep Learning Models for Unsupervised and Transfer Learning.

Degree: PhD, 2017, University of Toronto

 This thesis is a compilation of five research contributions whose goal is to do unsupervised and transfer learning by designing models that learn distributed representations… (more)

Subjects/Keywords: Boltzmann Machines; Deep Learning; Machine Learning; Neural Networks; Unsupervised Learning; 0984

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Srivastava, N. (2017). Deep Learning Models for Unsupervised and Transfer Learning. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/80672

Chicago Manual of Style (16th Edition):

Srivastava, Nitish. “Deep Learning Models for Unsupervised and Transfer Learning.” 2017. Doctoral Dissertation, University of Toronto. Accessed November 26, 2020. http://hdl.handle.net/1807/80672.

MLA Handbook (7th Edition):

Srivastava, Nitish. “Deep Learning Models for Unsupervised and Transfer Learning.” 2017. Web. 26 Nov 2020.

Vancouver:

Srivastava N. Deep Learning Models for Unsupervised and Transfer Learning. [Internet] [Doctoral dissertation]. University of Toronto; 2017. [cited 2020 Nov 26]. Available from: http://hdl.handle.net/1807/80672.

Council of Science Editors:

Srivastava N. Deep Learning Models for Unsupervised and Transfer Learning. [Doctoral Dissertation]. University of Toronto; 2017. Available from: http://hdl.handle.net/1807/80672

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

Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 November 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 Nov 2020.

Vancouver:

STUART C. Applying Machine Learning to Identify Maintenance Level for Software Releases . [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2020 Nov 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


Rochester Institute of Technology

29. Karnam, Srivallabha. Self-Supervised Learning for Segmentation using Image Reconstruction.

Degree: MS, Computer Engineering, 2020, Rochester Institute of Technology

  Deep learning is the engine that is piloting tremendous growth in various segments of the industry by consuming valuable fuel called data. We are… (more)

Subjects/Keywords: Classification; Computer vision; Self-supervised learning; Semantic segmentation; Unsupervised learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Karnam, S. (2020). Self-Supervised Learning for Segmentation using Image Reconstruction. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10532

Chicago Manual of Style (16th Edition):

Karnam, Srivallabha. “Self-Supervised Learning for Segmentation using Image Reconstruction.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed November 26, 2020. https://scholarworks.rit.edu/theses/10532.

MLA Handbook (7th Edition):

Karnam, Srivallabha. “Self-Supervised Learning for Segmentation using Image Reconstruction.” 2020. Web. 26 Nov 2020.

Vancouver:

Karnam S. Self-Supervised Learning for Segmentation using Image Reconstruction. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2020 Nov 26]. Available from: https://scholarworks.rit.edu/theses/10532.

Council of Science Editors:

Karnam S. Self-Supervised Learning for Segmentation using Image Reconstruction. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10532


Rochester Institute of Technology

30. Shembekar, Parikshit Prashant. Anomaly Detection in Videos through Deep Unsupervised Techniques.

Degree: MS, Computer Science (GCCIS), 2020, Rochester Institute of Technology

  Identifying abnormality in videos is an area of active research. Most of the work makes extensive use of supervised approaches, even though these methods… (more)

Subjects/Keywords: Anomaly detection; Deep learning; Probabilistic graphical models; Unsupervised learning

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Shembekar, P. P. (2020). Anomaly Detection in Videos through Deep Unsupervised Techniques. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/10572

Chicago Manual of Style (16th Edition):

Shembekar, Parikshit Prashant. “Anomaly Detection in Videos through Deep Unsupervised Techniques.” 2020. Masters Thesis, Rochester Institute of Technology. Accessed November 26, 2020. https://scholarworks.rit.edu/theses/10572.

MLA Handbook (7th Edition):

Shembekar, Parikshit Prashant. “Anomaly Detection in Videos through Deep Unsupervised Techniques.” 2020. Web. 26 Nov 2020.

Vancouver:

Shembekar PP. Anomaly Detection in Videos through Deep Unsupervised Techniques. [Internet] [Masters thesis]. Rochester Institute of Technology; 2020. [cited 2020 Nov 26]. Available from: https://scholarworks.rit.edu/theses/10572.

Council of Science Editors:

Shembekar PP. Anomaly Detection in Videos through Deep Unsupervised Techniques. [Masters Thesis]. Rochester Institute of Technology; 2020. Available from: https://scholarworks.rit.edu/theses/10572

[1] [2] [3] [4] [5] … [16]

.