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You searched for subject:(Covariate Shift). Showing records 1 – 6 of 6 total matches.

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University of Alberta

1. Wen, Junfeng. Robust Learning under Uncertain Test Distributions.

Degree: MS, Department of Computing Science, 2013, University of Alberta

 Many learning situations involve learning the conditional distribution p(y|x) when the training data is drawn from the training distribution ptr(x), even though it will later… (more)

Subjects/Keywords: Covariate Shift; Robust Learning; Machine Learning

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APA (6th Edition):

Wen, J. (2013). Robust Learning under Uncertain Test Distributions. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/tq57nr04p

Chicago Manual of Style (16th Edition):

Wen, Junfeng. “Robust Learning under Uncertain Test Distributions.” 2013. Masters Thesis, University of Alberta. Accessed December 15, 2019. https://era.library.ualberta.ca/files/tq57nr04p.

MLA Handbook (7th Edition):

Wen, Junfeng. “Robust Learning under Uncertain Test Distributions.” 2013. Web. 15 Dec 2019.

Vancouver:

Wen J. Robust Learning under Uncertain Test Distributions. [Internet] [Masters thesis]. University of Alberta; 2013. [cited 2019 Dec 15]. Available from: https://era.library.ualberta.ca/files/tq57nr04p.

Council of Science Editors:

Wen J. Robust Learning under Uncertain Test Distributions. [Masters Thesis]. University of Alberta; 2013. Available from: https://era.library.ualberta.ca/files/tq57nr04p


University of Manchester

2. Zennaro, Fabio. Feature distribution learning for covariate shift adaptation using sparse filtering.

Degree: PhD, 2017, University of Manchester

 This thesis studies a family of unsupervised learning algorithms called feature distribution learning and their extension to perform covariate shift adaptation. Unsupervised learning is one… (more)

Subjects/Keywords: 006.3; covariate shift adaptation; periodic sparse filtering; sparse filterning; unsupervised learning; covariate shift; feature distribution learning

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

APA (6th Edition):

Zennaro, F. (2017). Feature distribution learning for covariate shift adaptation using sparse filtering. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/feature-distribution-learning-for-covariate-shift-adaptation-using-sparse-filtering(67989db2-b8a0-4fac-8832-f611e9236ed5).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728046

Chicago Manual of Style (16th Edition):

Zennaro, Fabio. “Feature distribution learning for covariate shift adaptation using sparse filtering.” 2017. Doctoral Dissertation, University of Manchester. Accessed December 15, 2019. https://www.research.manchester.ac.uk/portal/en/theses/feature-distribution-learning-for-covariate-shift-adaptation-using-sparse-filtering(67989db2-b8a0-4fac-8832-f611e9236ed5).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728046.

MLA Handbook (7th Edition):

Zennaro, Fabio. “Feature distribution learning for covariate shift adaptation using sparse filtering.” 2017. Web. 15 Dec 2019.

Vancouver:

Zennaro F. Feature distribution learning for covariate shift adaptation using sparse filtering. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Dec 15]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/feature-distribution-learning-for-covariate-shift-adaptation-using-sparse-filtering(67989db2-b8a0-4fac-8832-f611e9236ed5).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728046.

Council of Science Editors:

Zennaro F. Feature distribution learning for covariate shift adaptation using sparse filtering. [Doctoral Dissertation]. University of Manchester; 2017. Available from: https://www.research.manchester.ac.uk/portal/en/theses/feature-distribution-learning-for-covariate-shift-adaptation-using-sparse-filtering(67989db2-b8a0-4fac-8832-f611e9236ed5).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728046


University of Illinois – Chicago

3. Fei, Geli. Open Classification and Change Detection in the Similarity Space.

Degree: 2017, University of Illinois – Chicago

 The rapid emergence of new topics and the highly diverse nature of online text data have brought new challenges to existing text classification techniques. One… (more)

Subjects/Keywords: Open classification; Covariate shift; Cumulative learning; Spam detection; Change detection

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

APA (6th Edition):

Fei, G. (2017). Open Classification and Change Detection in the Similarity Space. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21802

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):

Fei, Geli. “Open Classification and Change Detection in the Similarity Space.” 2017. Thesis, University of Illinois – Chicago. Accessed December 15, 2019. http://hdl.handle.net/10027/21802.

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

MLA Handbook (7th Edition):

Fei, Geli. “Open Classification and Change Detection in the Similarity Space.” 2017. Web. 15 Dec 2019.

Vancouver:

Fei G. Open Classification and Change Detection in the Similarity Space. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10027/21802.

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

Council of Science Editors:

Fei G. Open Classification and Change Detection in the Similarity Space. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21802

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


University of Illinois – Chicago

4. Chen, Xiangli. Robust Structured Prediction for Process Data.

Degree: 2017, University of Illinois – Chicago

 Processes involve a series of actions performed to achieve a particular result. Developing prediction models for process data is important for many real problems such… (more)

Subjects/Keywords: Structured Prediction; Optimal Control; Reinforcement Learning; Inverse Reinforcement Learning; Imitation Learning; Regression; Covariate Shift

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

APA (6th Edition):

Chen, X. (2017). Robust Structured Prediction for Process Data. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21987

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):

Chen, Xiangli. “Robust Structured Prediction for Process Data.” 2017. Thesis, University of Illinois – Chicago. Accessed December 15, 2019. http://hdl.handle.net/10027/21987.

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

MLA Handbook (7th Edition):

Chen, Xiangli. “Robust Structured Prediction for Process Data.” 2017. Web. 15 Dec 2019.

Vancouver:

Chen X. Robust Structured Prediction for Process Data. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10027/21987.

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

Council of Science Editors:

Chen X. Robust Structured Prediction for Process Data. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21987

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

5. Zennaro, Fabio. Feature Distribution Learning for Covariate Shift Adaptation Using Sparse Filtering.

Degree: 2017, University of Manchester

 This thesis studies a family of unsupervised learning algorithms called feature distribution learning and their extension to perform covariate shift adaptation. Unsupervised learning is one… (more)

Subjects/Keywords: unsupervised learning; covariate shift; feature distribution learning; sparse filterning; covariate shift adaptation; periodic sparse filtering

…Abbreviations AE Auto-Encoders. CD Cross-domain Dierence. CSA Covariate Shift… …for Covariate Shift Adaptation Using Sparse Filtering Fabio Massimo Zennaro A thesis… …perform covariate shift adaptation. feature distribution learn- Unsupervised learning is one… …traditional algorithms may be severely compromised. Covariate shift adaptation has then developed as… …a lively sub-eld concerned with designing algorithms that can account for covariate shift… 

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

APA (6th Edition):

Zennaro, F. (2017). Feature Distribution Learning for Covariate Shift Adaptation Using Sparse Filtering. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312169

Chicago Manual of Style (16th Edition):

Zennaro, Fabio. “Feature Distribution Learning for Covariate Shift Adaptation Using Sparse Filtering.” 2017. Doctoral Dissertation, University of Manchester. Accessed December 15, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312169.

MLA Handbook (7th Edition):

Zennaro, Fabio. “Feature Distribution Learning for Covariate Shift Adaptation Using Sparse Filtering.” 2017. Web. 15 Dec 2019.

Vancouver:

Zennaro F. Feature Distribution Learning for Covariate Shift Adaptation Using Sparse Filtering. [Internet] [Doctoral dissertation]. University of Manchester; 2017. [cited 2019 Dec 15]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312169.

Council of Science Editors:

Zennaro F. Feature Distribution Learning for Covariate Shift Adaptation Using Sparse Filtering. [Doctoral Dissertation]. University of Manchester; 2017. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:312169

6. Que, Qichao. Integral Equations For Machine Learning Problems.

Degree: PhD, Computer Science and Engineering, 2016, The Ohio State University

 Supervised learning algorithms have achieved significant success in the last decade. To further improve learning performance, we still need to have a better understanding of… (more)

Subjects/Keywords: Computer Science; Machine Learning; RBF Networks; Supervised Learning; Kernel Methods; Fredholm Equations; Covariate Shift

…Assumption . . . . . . 49 50 51 5 Fredholm Equations for Covariate Shift… …57 5.1 Covariate Shift and Fredholm Integral Equation . . . . . . . . . . . . 5.1.1… …related to the problem of covariate shift in transfer learning. Our approach reformulates the… …covariate shift and demonstrate some encouraging experimental comparisons. More importantly, we… …introduced to address the problem of density ratio estimation and covariate shift. In that work… 

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

APA (6th Edition):

Que, Q. (2016). Integral Equations For Machine Learning Problems. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1461258998

Chicago Manual of Style (16th Edition):

Que, Qichao. “Integral Equations For Machine Learning Problems.” 2016. Doctoral Dissertation, The Ohio State University. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461258998.

MLA Handbook (7th Edition):

Que, Qichao. “Integral Equations For Machine Learning Problems.” 2016. Web. 15 Dec 2019.

Vancouver:

Que Q. Integral Equations For Machine Learning Problems. [Internet] [Doctoral dissertation]. The Ohio State University; 2016. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1461258998.

Council of Science Editors:

Que Q. Integral Equations For Machine Learning Problems. [Doctoral Dissertation]. The Ohio State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1461258998

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