Advanced Supervised And Semi Supervised Learning

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Advanced Supervised And Semi Supervised Learning

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Author : Massih-Reza Amini
language : en
Publisher: Springer Nature
Release Date : 2025-11-17

Advanced Supervised And Semi Supervised Learning written by Massih-Reza Amini and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-17 with Computers categories.

Machine learning is one of the leading areas of artificial intelligence. It concerns the study and development of quantitative models that enable a computer to carry out operations without having been expressly programmed to do so. In this situation, learning is about identifying complex shapes and making intelligent decisions. The challenge in completing this task, given all the available inputs, is that the set of potential decisions is typically quite difficult to enumerate. Machine learning algorithms have been developed with the goal of learning about the problem to be handled based on a collection of limited data from this problem in order to get around this challenge. This textbook presents the scientific foundations of supervised learning theory, the most widespread algorithms developed according to this framework, as well as the semi-supervised and the learning-to-rank frameworks, at a level accessible to master's students. The aim of the book is to provide a coherent presentation linking the theory to the algorithms developed in this field. In addition, this study is not limited to the presentation of these foundations, but it also presents exercises, and is intended for readers who seek to understand the functioning of these models sometimes designated as black boxes.

Advanced Supervised And Semi Supervised Learning

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Author : Massih-Reza Amini
language : en
Publisher: Springer
Release Date : 2025-10-09

Advanced Supervised And Semi Supervised Learning written by Massih-Reza Amini and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-09 with Computers categories.

Machine learning is one of the leading areas of artificial intelligence. It concerns the study and development of quantitative models that enable a computer to carry out operations without having been expressly programmed to do so. In this situation, learning is about identifying complex shapes and making intelligent decisions. The challenge in completing this task, given all the available inputs, is that the set of potential decisions is typically quite difficult to enumerate. Machine learning algorithms have been developed with the goal of learning about the problem to be handled based on a collection of limited data from this problem in order to get around this challenge. This textbook presents the scientific foundations of supervised learning theory, the most widespread algorithms developed according to this framework, as well as the semi-supervised and the learning-to-rank frameworks, at a level accessible to master's students. The aim of the book is to provide a coherent presentation linking the theory to the algorithms developed in this field. In addition, this study is not limited to the presentation of these foundations, but it also presents exercises, and is intended for readers who seek to understand the functioning of these models sometimes designated as black boxes.

Machine Learning Foundations

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Author : Taeho Jo
language : en
Publisher: Springer Nature
Release Date : 2021-02-12

Machine Learning Foundations written by Taeho Jo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-12 with Technology & Engineering categories.

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.

Advanced Materials And Computer Science

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Author : Garry Zhu
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2011-04-19

Advanced Materials And Computer Science written by Garry Zhu and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-19 with Technology & Engineering categories.

Selected, peer reviewed paper from 2011 International Conference on Advanced Materials and Computer Science (ICAMCS 2011), May 1-2, 2010 in Chengdu, China

Database Systems For Advanced Applications

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Author :
language : en
Publisher:
Release Date : 2005

Database Systems For Advanced Applications written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Database design categories.

Computational Materials Science

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Author : Feng Xiong
language : en
Publisher: Trans Tech Publications Ltd
Release Date : 2011-07-04

Computational Materials Science written by Feng Xiong and has been published by Trans Tech Publications Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-04 with Technology & Engineering categories.

Selected, peer reviewed papers from the 2011 International Conference on Computational Materials Science (CMS 2011) in April 17-18, Guangzhou, China

College Of Engineering University Of Michigan Publications

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Author : University of Michigan. College of Engineering
language : en
Publisher:
Release Date : 2011

College Of Engineering University Of Michigan Publications written by University of Michigan. College of Engineering and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Catalogs, College categories.

Also contains brochures, directories, manuals, and programs from various College of Engineering student organizations such as the Society of Women Engineers and Tau Beta Pi.

Introduction To Semi Supervised Learning

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Author : Xiaojin Zhu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31

Introduction To Semi Supervised Learning written by Xiaojin Zhu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook

Machine Learning Ecml

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Author :
language : en
Publisher:
Release Date : 2004

Machine Learning Ecml written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Induction (Logic) categories.

Introduction To Machine Learning

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Author : Ethem Alpaydin
language : en
Publisher: MIT Press (MA)
Release Date : 2010

Introduction To Machine Learning written by Ethem Alpaydin and has been published by MIT Press (MA) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.

A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions.