Machine Learning Methods - Hardcover

Li, Hang

 
9789819939169: Machine Learning Methods
Hardcover
ISBN 10: 981993916X ISBN 13: 9789819939169
Publisher: Springer, 2023

Synopsis

This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis.
As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.

"synopsis" may belong to another edition of this title.

About the Author

Hang Li is Head of Research, Bytedance Technology. He is an ACM Fellow, an ACL Fellow, and an IEEE Fellow. His research areas include natural language processing, information retrieval, machine learning, and data mining. Hang graduated from Kyoto University in 1988 and earned his PhD from the University of Tokyo in 1998. He worked at NEC Research as researcher from 1990 to 2001, Microsoft Research Asia as senior researcher and research manager from 2001 to 2012, and chief scientist and director of Huawei Noah’s Ark Lab from 2012 to 2017. He joined Bytedance in 2017. Hang has published four technical books, and more than 140 technical papers at top international conferences.

From the Back Cover

This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis.
As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2023
Language
English
ISBN 10 
981993916X
ISBN 13 
9789819939169
Binding
Hardcover
Edition number
1
Number of pages
547

Other Popular Editions of the Same Title

9789819939190: Machine Learning Methods

Featured Edition

ISBN 10:  9819939194 ISBN 13:  9789819939190
Publisher: Springer, 2024
Softcover