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Subject: Statistics.

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Purdue University. Computer Sciences. degree granting institution.

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University of California, Los Angeles. Statistics 0891. degree granting institution.

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Barkley, Brian G., author.

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Birnbaum, Howard G. editor.

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Boston College. Finance. degree granting institution.

(1)

Columbia University. Statistics. degree granting institution.

(1)

Comes, Eric Navarro, author.

(1)

Cui, Yifan, author.

(1)

Ebrahimzadeh Houlasou, Ehsan, author.

(1)

Fang, Youhan, author.

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Gao, Christian Siyao, author.

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Greenberg, Paul E. editor.

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Illinois Institute of Technology. Electrical and Computer Engineering. degree granting institution.

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Iowa State University. Statistics. degree granting institution.

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Jiang, Zhehan, author. (orcid)0000-0002-1376-9439

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Koçak, Mustafa Anil, author.

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Li, Quan, author.

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Ma, Chenjie, author.

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Ma, Chong, author.

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Massachusetts Institute of Technology. degree granting institution.

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Mena, Gonzalo E., author.

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New York University Tandon School of Engineering. Electrical and Computer Engineering. degree granting institution.

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Nijkamp, Erik Lennart, author.

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Northeastern University. Department of Game Design. degree granting institution.

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Old Dominion University. Biological Sciences. degree granting institution.

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Olson, Matthew, author.

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Robertson, Joseph Carver, author.

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Sage, Andrew John, author. (orcid)0000-0003-4656-9748

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Shirman, Aleksandra, author.

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Sisson, Nicholas B., author. (orcid)0000-0003-0066-9225

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South Dakota State University. Mathematics & Statistics. degree granting institution.

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SpringerLink (Online service)

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The University of Chicago. Statistics. degree granting institution.

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The University of North Carolina at Chapel Hill. Biostatistics. degree granting institution.

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The University of North Carolina at Chapel Hill. Statistics and Operations Research. degree granting institution.

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University of California, Los Angeles. Electrical Engineering 0303. degree granting institution.

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University of California, San Diego. Physics. degree granting institution.

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University of Colorado at Boulder. Applied Mathematics. degree granting institution.

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University of Kansas. Biostatistics. degree granting institution.

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University of Kansas. Psychology & Research in Education. degree granting institution.

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University of Pennsylvania. Statistics. degree granting institution.

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University of South Carolina. Statistics. degree granting institution.

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University of Southern California. Statistics. degree granting institution.

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University of Wyoming. Statistics. degree granting institution.

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Wang, Qi, author.

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Wang, Wenzhi, author.

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Wong, Sze Wai, author.

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Woytarowicz, Nicole, author.

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Wu, Tao, author.

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Yin, Tianzhixi, author.

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Cover image for Application of Time Series Analysis and Machine Learning on Stock Forecasting

Application of Time Series Analysis and Machine Learning on Stock Forecasting

1.

Application of Time Series Analysis and Machine Learning on Stock Forecasting

by

Ma, Chenjie, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10801614

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Excerpt:

Application of Time Series Analysis and Machine Learning on Stock Forecasting / Ma, Chenjie, author.

Cover image for Machine Learning Methods for Predicting Global and Local Crime in an Urban Area

Machine Learning Methods for Predicting Global and Local Crime in an Urban Area

2.

Machine Learning Methods for Predicting Global and Local Crime in an Urban Area

by

Comes, Eric Navarro, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10807821

Format:

Excerpt:

Machine Learning Methods for Predicting Global and Local Crime in an Urban Area / Comes, Eric

Cover image for Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data

Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data

3.

Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data

by

Mena, Gonzalo E., author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10830688

Format:

Excerpt:

Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data / Mena, Gonzalo E

Cover image for A Dance with the Langevin Equation

A Dance with the Langevin Equation

4.

A Dance with the Langevin Equation

by

Nijkamp, Erik Lennart, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10830061

Format:

Excerpt:

Statistical Physics, we show novel adaptions to the field of Machine Learning. In particular, we elaborate

Cover image for Essays on Random Forest Ensembles

Essays on Random Forest Ensembles

5.

Essays on Random Forest Ensembles

by

Olson, Matthew, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10786136

Format:

Excerpt:

A random forest is a popular machine learning ensemble method that has proven successful in solving

Cover image for Applying Particle Swarm Optimization to Estimate Psychometric Models with Categorical Responses

Applying Particle Swarm Optimization to Estimate Psychometric Models with Categorical Responses

6.

Applying Particle Swarm Optimization to Estimate Psychometric Models with Categorical Responses

by

Jiang, Zhehan, author. (orcid)0000-0002-1376-9439

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10744846

Format:

Excerpt:

has been widely used in machine learning fields but remains less-known in the psychometrics community

Cover image for Higher-order Random Walk Methods for Data Analysis

Higher-order Random Walk Methods for Data Analysis

7.

Higher-order Random Walk Methods for Data Analysis

by

Wu, Tao, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10790747

Format:

Excerpt:

Markov random walk models are powerful analytical tools for multiple areas in machine learning

Cover image for The Impact of Data Sovereignty on American Indian Self-Determination: A Framework Proof of Concept Using Data Science

The Impact of Data Sovereignty on American Indian Self-Determination: A Framework Proof of Concept Using Data Science

8.

The Impact of Data Sovereignty on American Indian Self-Determination: A Framework Proof of Concept Using Data Science

by

Robertson, Joseph Carver, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10811259

Format:

Excerpt:

statistics, exploratory data analysis, and machine learning. And although these case studies are first

Cover image for Feature Selection and Classification for High-Dimensional Biological Data Under Cross-Validation Framework

Feature Selection and Classification for High-Dimensional Biological Data Under Cross-Validation Framework

9.

Feature Selection and Classification for High-Dimensional Biological Data Under Cross-Validation Framework

by

Zhong, Yi, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10817332

Format:

Excerpt:

cross validation techniques. Also, several types of machine learning methods such as lasso, support

Cover image for Random Forest Robustness, Variable Importance, and Tree Aggregation

Random Forest Robustness, Variable Importance, and Tree Aggregation

10.

Random Forest Robustness, Variable Importance, and Tree Aggregation

by

Sage, Andrew John, author. (orcid)0000-0003-4656-9748

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10789056

Format:

Excerpt:

Random forest methodology is a nonparametric, machine learning approach capable of strong

Cover image for Classification with Hash Collision Networks

Classification with Hash Collision Networks

11.

Classification with Hash Collision Networks

by

Gao, Christian Siyao, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10827049

Format:

Excerpt:

Today most of our machine learning methods and classification methods are gradient based methods

Cover image for Discretizing Continuous Attributes of a Bayesian Network with a Birth and Death Process Based on Minimum Description Length

Discretizing Continuous Attributes of a Bayesian Network with a Birth and Death Process Based on Minimum Description Length

12.

Discretizing Continuous Attributes of a Bayesian Network with a Birth and Death Process Based on Minimum Description Length

by

Woytarowicz, Nicole, author.

http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:10809965

Format:

Excerpt:

International Conference on Machine Learning, pp. 157--165, (1996)] where a principled approach was developed

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