Machine learning methods

Hang Li (Autor)

Buch | Hardcover

532 Seiten

2024 | 1. Auflage
Springer Verlag Singapore
978-981-99-3916-9 (ISBN)

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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.

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.

Chapter 1 Introduction to Machine learning and Supervised Learning.- Chapter 2 Perceptron.- Chapter 3 K-Nearest-Neighbor.- Chapter 4 The Naïve Bayes Method.- Chapter 5 Decision Tree.- Chapter 6 Logistic Regression and Maximum Entropy Model.- Chapter 7 Support Vector Machine.- Chapter 8 Boosting.- Chapter 9 EM Algorithm and Its Extensions.- Chapter 10 Hidden Markov Model.- Chapter 11 Conditional Random Field.

Erscheinungsdatum	09.12.2023
Übersetzer	Lu Lin, Huanqiang Zeng
Zusatzinfo	Illustrationen
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Gewicht	979 g
Einbandart	gebunden
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
Schlagworte	classification • Decision Tree • EM algorithm • Hidden Markov Model • K-means clustering • K-Nearest-Neighbors • Latent Dirichlet Allocation • latent semantic analysis • Logistic Regression Model • machine learning • Markov Chain Monte Carlo Algorithms • PageRank Algorithm • Perceptron Model • Principal Component Analysis • Regression • Singular Value Decompostition (SVD) • Statistical Learning • supervised learning • Support Vector Machines • Unsupervised Learning
ISBN-10	981-99-3916-X / 981993916X
ISBN-13	978-981-99-3916-9 / 9789819939169
Zustand	Neuware