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Advanced Machine Learning Methods: S...

Zhang, Xinwei.

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Advanced Machine Learning Methods: Semi-Supervised Learning, Multi-Category Classification, and Energy-Based Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced Machine Learning Methods: Semi-Supervised Learning, Multi-Category Classification, and Energy-Based Models./
Author:	Zhang, Xinwei.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	207 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30637513
ISBN:	9798380851749

Advanced Machine Learning Methods: Semi-Supervised Learning, Multi-Category Classification, and Energy-Based Models.
Zhang, Xinwei.

Advanced Machine Learning Methods: Semi-Supervised Learning, Multi-Category Classification, and Energy-Based Models. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 207 p.

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2023.

This item must not be sold to any third party vendors.

This dissertation explores diverse aspects of machine learning with a particular focus on semi-supervised learning, multi-category classification, and energy-based models. The work begins by extending the statistical equivalence between logistic regression and exponential tilt modeling, leading to the development of a novel semi-supervised logistic learning method. Enhanced prediction accuracy is achieved by leveraging both labeled and unlabeled data. Next, we delve into the construction of loss functions and the establishment of corresponding regret bounds for multi-category classification. Here, new general representations of losses are derived, leading to the discovery of new hinge-like convex losses and multi-class proper scoring rules. In the final part, we introduce diffusion data and propose to learn a joint energy-based model, through persistent training, which simultaneously achieves long-run stability, post-training image generation, and superior out-of-distribution detection. Overall, this dissertation provides valuable insights and advancements in the field of machine learning, revealing connections between different methods and contributing to the improvement of existing methods and theories.

ISBN: 9798380851749Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Energy-based models

Advanced Machine Learning Methods: Semi-Supervised Learning, Multi-Category Classification, and Energy-Based Models.
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500 $a Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
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502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey, School of Graduate Studies, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation explores diverse aspects of machine learning with a particular focus on semi-supervised learning, multi-category classification, and energy-based models. The work begins by extending the statistical equivalence between logistic regression and exponential tilt modeling, leading to the development of a novel semi-supervised logistic learning method. Enhanced prediction accuracy is achieved by leveraging both labeled and unlabeled data. Next, we delve into the construction of loss functions and the establishment of corresponding regret bounds for multi-category classification. Here, new general representations of losses are derived, leading to the discovery of new hinge-like convex losses and multi-class proper scoring rules. In the final part, we introduce diffusion data and propose to learn a joint energy-based model, through persistent training, which simultaneously achieves long-run stability, post-training image generation, and superior out-of-distribution detection. Overall, this dissertation provides valuable insights and advancements in the field of machine learning, revealing connections between different methods and contributing to the improvement of existing methods and theories.
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650 4 $a Computer science. $3 523869
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653 $a Multicategory classification
653 $a Semi-supervised learning
653 $a Machine learning
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710 2 $a Rutgers The State University of New Jersey, School of Graduate Studies. $b Statistics and Biostatistics. $3 3545045
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