Machine Learning Methods / by Hang Li.

Format:

Book

Author/Creator:

Li, Hang.

Contributor:

Lin, Lu.

Zeng, Huanqiang.

Series:

Computer Science Series

Language:

English

Subjects (All):

Machine learning.

Machine Learning.

Statistical Learning.

Local Subjects:

Machine Learning.

Statistical Learning.

Physical Description:

1 online resource (530 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9939-17-8

OCLC:

1412621012