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You searched for subject:(Unsupervised learning). Showing records 1 – 30 of 294 total matches.

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Université de Neuchâtel

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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Kocher M. Text clustering with styles. [Internet] [Thesis]. Université de Neuchâtel; 2017. [cited 2019 Apr 21]. 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 April 21, 2019. http://hdl.handle.net/10063/6951.

MLA Handbook (7th Edition):

Butler-Yeoman, Tony. “Learning to Disentangle the Complex Causes of Data.” 2017. Web. 21 Apr 2019.

Vancouver:

Butler-Yeoman T. Learning to Disentangle the Complex Causes of Data. [Internet] [Masters thesis]. Victoria University of Wellington; 2017. [cited 2019 Apr 21]. 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


University of British Columbia

3. Koepke, Hoyt Adam. Bayesian cluster validation .

Degree: 2008, University of British Columbia

 We propose a novel framework based on Bayesian principles for validating clusterings and present efficient algorithms for use with centroid or exemplar based clustering solutions.… (more)

Subjects/Keywords: Clustering; Cluster validation; Unsupervised learning

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APA (6th Edition):

Koepke, H. A. (2008). Bayesian cluster validation . (Thesis). University of British Columbia. Retrieved from http://hdl.handle.net/2429/1496

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):

Koepke, Hoyt Adam. “Bayesian cluster validation .” 2008. Thesis, University of British Columbia. Accessed April 21, 2019. http://hdl.handle.net/2429/1496.

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

MLA Handbook (7th Edition):

Koepke, Hoyt Adam. “Bayesian cluster validation .” 2008. Web. 21 Apr 2019.

Vancouver:

Koepke HA. Bayesian cluster validation . [Internet] [Thesis]. University of British Columbia; 2008. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2429/1496.

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

Council of Science Editors:

Koepke HA. Bayesian cluster validation . [Thesis]. University of British Columbia; 2008. Available from: http://hdl.handle.net/2429/1496

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

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 April 21, 2019. 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. 21 Apr 2019.

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 2019 Apr 21]. 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 April 21, 2019. 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. 21 Apr 2019.

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 2019 Apr 21]. 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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Im J. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems . [Internet] [Thesis]. University of Guelph; 2015. [cited 2019 Apr 21]. 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 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

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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 April 21, 2019. http://hdl.handle.net/1807/89800.

MLA Handbook (7th Edition):

Makhzani, Alireza. “Unsupervised Representation Learning with Autoencoders.” 2018. Web. 21 Apr 2019.

Vancouver:

Makhzani A. Unsupervised Representation Learning with Autoencoders. [Internet] [Doctoral dissertation]. University of Toronto; 2018. [cited 2019 Apr 21]. 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

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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 April 21, 2019. https://era.library.ualberta.ca/files/gq67jt70x.

MLA Handbook (7th Edition):

White, Martha. “Regularized factor models.” 2014. Web. 21 Apr 2019.

Vancouver:

White M. Regularized factor models. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2019 Apr 21]. 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

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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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Bod G. Self-taught learning: Implementation using MATLAB . [Internet] [Thesis]. University of Debrecen; 2014. [cited 2019 Apr 21]. 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

10. 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 (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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Xu J. On-line and unsupervised learning for codebook based visual recognition. [Internet] [Doctoral dissertation]. University of New South Wales; 2011. [cited 2019 Apr 21]. 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 Texas – Austin

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

Degree: 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. (Thesis). 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
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/63489.

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

MLA Handbook (7th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Web. 21 Apr 2019.

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] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 21]. 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
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-6888-3095. Embodied learning for visual recognition. [Thesis]. 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
Not specified: Masters Thesis or Doctoral Dissertation


University of Connecticut

12. 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 (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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2019 Apr 21]. 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 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 · 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/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 April 21, 2019. 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. 21 Apr 2019.

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 2019 Apr 21]. 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


Halmstad University

14. Grubinger, Thomas. Knowledge Extraction from Logged Truck Data using Unsupervised Learning Methods.

Degree: Computer and Electrical Engineering (IDE), 2008, Halmstad University

  The goal was to extract knowledge from data that is logged by the electronic system of every Volvo truck. This allowed the evaluation of… (more)

Subjects/Keywords: Unsupervised learning; logged vehicle data; Knowledge extraction

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

APA (6th Edition):

Grubinger, T. (2008). Knowledge Extraction from Logged Truck Data using Unsupervised Learning Methods. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1147

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):

Grubinger, Thomas. “Knowledge Extraction from Logged Truck Data using Unsupervised Learning Methods.” 2008. Thesis, Halmstad University. Accessed April 21, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1147.

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

MLA Handbook (7th Edition):

Grubinger, Thomas. “Knowledge Extraction from Logged Truck Data using Unsupervised Learning Methods.” 2008. Web. 21 Apr 2019.

Vancouver:

Grubinger T. Knowledge Extraction from Logged Truck Data using Unsupervised Learning Methods. [Internet] [Thesis]. Halmstad University; 2008. [cited 2019 Apr 21]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1147.

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

Council of Science Editors:

Grubinger T. Knowledge Extraction from Logged Truck Data using Unsupervised Learning Methods. [Thesis]. Halmstad University; 2008. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-1147

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

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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 April 21, 2019. 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. 21 Apr 2019.

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 2019 Apr 21]. 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


University of Houston

16. Xu, Yan. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.

Degree: Electrical and Computer Engineering, Department of, 2015, University of Houston

 The goal of this dissertation is to develop unsupervised algorithms for discovering previously unknown subspace trends in massive multivariate biomedical data sets without the benefit… (more)

Subjects/Keywords: trend; visualization; biomedical; unsupervised learning; feature selection

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APA (6th Edition):

Xu, Y. (2015). Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. (Thesis). University of Houston. Retrieved from http://hdl.handle.net/10657/3672

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):

Xu, Yan. “Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.” 2015. Thesis, University of Houston. Accessed April 21, 2019. http://hdl.handle.net/10657/3672.

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

MLA Handbook (7th Edition):

Xu, Yan. “Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets.” 2015. Web. 21 Apr 2019.

Vancouver:

Xu Y. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. [Internet] [Thesis]. University of Houston; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10657/3672.

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

Council of Science Editors:

Xu Y. Unsupervised Discovery and Representation of Subspace Trends in Massive Biomedical Datasets. [Thesis]. University of Houston; 2015. Available from: http://hdl.handle.net/10657/3672

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

17. Zeltner, Felix. Autonomous Terrain Classification Through Unsupervised Learning.

Degree: Electrical and Space Engineering, 2016, Luleå University of Technology

  A key component of autonomous outdoor navigation in unstructured environments is the classification of terrain. Recent development in the area of machine learning show… (more)

Subjects/Keywords: Terrain Classification; Unsupervised Learning; Robotics; Neural Networks

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

APA (6th Edition):

Zeltner, F. (2016). Autonomous Terrain Classification Through Unsupervised Learning. (Thesis). Luleå University of Technology. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60893

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):

Zeltner, Felix. “Autonomous Terrain Classification Through Unsupervised Learning.” 2016. Thesis, Luleå University of Technology. Accessed April 21, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60893.

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

MLA Handbook (7th Edition):

Zeltner, Felix. “Autonomous Terrain Classification Through Unsupervised Learning.” 2016. Web. 21 Apr 2019.

Vancouver:

Zeltner F. Autonomous Terrain Classification Through Unsupervised Learning. [Internet] [Thesis]. Luleå University of Technology; 2016. [cited 2019 Apr 21]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60893.

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

Council of Science Editors:

Zeltner F. Autonomous Terrain Classification Through Unsupervised Learning. [Thesis]. Luleå University of Technology; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60893

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


Universidade Nova

18. 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 · 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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Madsen JH. Outlier detection for improved clustering : empirical research for unsupervised data mining. [Internet] [Thesis]. Universidade Nova; 2018. [cited 2019 Apr 21]. 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


George Mason University

19. 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 · 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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Wang P. Nonparametric Bayesian Models for Unsupervised Learning . [Internet] [Thesis]. George Mason University; 2011. [cited 2019 Apr 21]. 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 Illinois – Urbana-Champaign

20. 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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Riaz M. An unsupervised approach to identifying causal relations from relevant scenarios. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2010. [cited 2019 Apr 21]. 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 Waterloo

21. Pal, David. Contributions to Unsupervised and Semi-Supervised Learning.

Degree: 2009, University of Waterloo

 This thesis studies two problems in theoretical machine learning. The first part of the thesis investigates the statistical stability of clustering algorithms. In the second… (more)

Subjects/Keywords: machine learning; statistics; unsupervised learning; semi-supervised learning; learning theory

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

APA (6th Edition):

Pal, D. (2009). Contributions to Unsupervised and Semi-Supervised Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/4445

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):

Pal, David. “Contributions to Unsupervised and Semi-Supervised Learning.” 2009. Thesis, University of Waterloo. Accessed April 21, 2019. http://hdl.handle.net/10012/4445.

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

MLA Handbook (7th Edition):

Pal, David. “Contributions to Unsupervised and Semi-Supervised Learning.” 2009. Web. 21 Apr 2019.

Vancouver:

Pal D. Contributions to Unsupervised and Semi-Supervised Learning. [Internet] [Thesis]. University of Waterloo; 2009. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/10012/4445.

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

Council of Science Editors:

Pal D. Contributions to Unsupervised and Semi-Supervised Learning. [Thesis]. University of Waterloo; 2009. Available from: http://hdl.handle.net/10012/4445

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


Linköping University

22. 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 · 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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Internet] [Thesis]. Linköping University; 2011. [cited 2019 Apr 21]. 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 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

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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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Vladymyrov M. Large-Scale Methods for Nonlinear Manifold Learning. [Internet] [Thesis]. University of California – Merced; 2014. [cited 2019 Apr 21]. 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


University of Toronto

24. 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

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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 April 21, 2019. http://hdl.handle.net/1807/80672.

MLA Handbook (7th Edition):

Srivastava, Nitish. “Deep Learning Models for Unsupervised and Transfer Learning.” 2017. Web. 21 Apr 2019.

Vancouver:

Srivastava N. Deep Learning Models for Unsupervised and Transfer Learning. [Internet] [Doctoral dissertation]. University of Toronto; 2017. [cited 2019 Apr 21]. 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


University of Edinburgh

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

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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Heess NMO. Learning generative models of mid-level structure in natural images. [Internet] [Doctoral dissertation]. University of Edinburgh; 2012. [cited 2019 Apr 21]. 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


University of Manchester

26. 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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Parkinson JC. Representation learning with a temporally coherent mixed-representation. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Apr 21]. 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

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

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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Sener O. Learning From Large-Scale Visual Data For Robots . [Internet] [Thesis]. Cornell University; 2016. [cited 2019 Apr 21]. 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


Kansas State University

28. Varshney, Varun. Supervised and unsupervised learning for plant and crop row detection in precision agriculture.

Degree: MS, Department of Computing and Information Sciences, 2017, Kansas State University

 The goal of this research is to present a comparison between different clustering and segmentation techniques, both supervised and unsupervised, to detect plant and crop… (more)

Subjects/Keywords: precision agriculture; deep learning; machine learning; supervised; unsupervised

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

APA (6th Edition):

Varshney, V. (2017). Supervised and unsupervised learning for plant and crop row detection in precision agriculture. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/35463

Chicago Manual of Style (16th Edition):

Varshney, Varun. “Supervised and unsupervised learning for plant and crop row detection in precision agriculture.” 2017. Masters Thesis, Kansas State University. Accessed April 21, 2019. http://hdl.handle.net/2097/35463.

MLA Handbook (7th Edition):

Varshney, Varun. “Supervised and unsupervised learning for plant and crop row detection in precision agriculture.” 2017. Web. 21 Apr 2019.

Vancouver:

Varshney V. Supervised and unsupervised learning for plant and crop row detection in precision agriculture. [Internet] [Masters thesis]. Kansas State University; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2097/35463.

Council of Science Editors:

Varshney V. Supervised and unsupervised learning for plant and crop row detection in precision agriculture. [Masters Thesis]. Kansas State University; 2017. Available from: http://hdl.handle.net/2097/35463


University of Washington

29. McQueen, James. Scalable Manifold Learning and Related Topics.

Degree: PhD, 2017, University of Washington

 The subject of manifold learning is vast and still largely unexplored. As a subset of unsupervised learning it has a fundamental challenge in adequately defining… (more)

Subjects/Keywords: Clustering; Machine Learning; Manifold Learning; Non-Linear Dimension Reduction; Unsupervised Learning; Statistics; Statistics

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

APA (6th Edition):

McQueen, J. (2017). Scalable Manifold Learning and Related Topics. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/40305

Chicago Manual of Style (16th Edition):

McQueen, James. “Scalable Manifold Learning and Related Topics.” 2017. Doctoral Dissertation, University of Washington. Accessed April 21, 2019. http://hdl.handle.net/1773/40305.

MLA Handbook (7th Edition):

McQueen, James. “Scalable Manifold Learning and Related Topics.” 2017. Web. 21 Apr 2019.

Vancouver:

McQueen J. Scalable Manifold Learning and Related Topics. [Internet] [Doctoral dissertation]. University of Washington; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/1773/40305.

Council of Science Editors:

McQueen J. Scalable Manifold Learning and Related Topics. [Doctoral Dissertation]. University of Washington; 2017. Available from: http://hdl.handle.net/1773/40305


University of Cambridge

30. 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 April 21, 2019. 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. 21 Apr 2019.

Vancouver:

Bui TD. Efficient deterministic approximate Bayesian inference for Gaussian process models. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Apr 21]. 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

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