Pattern recognition and machine learning for self-study I supervised learning Kenichiro Ishii, Naonori Ueda, Eisaku Maeda, Hiroshi Murase

Format:

Book

Author/Creator:

Ishii, Kenichiro, author.

Ueda, Naonori, author.

Maeda, Eisaku, author.

Murase, Hiroshi, author.

Series:

Springer Asia Pacific mathematics series ; 3091-2563 volume 1

Springer Asia Pacific mathematics series 3091-2563 volume 1

Language:

English

Subjects (All):

Pattern perception.

Machine learning.

Physical Description:

1 online resource

illustration

Edition:

1st ed.

Place of Publication:

Singapore Springer [2026]

Summary:

This book explains the basic principles of pattern recognition (PR) and machine learning (ML) in an easy-to-understand manner for beginners who are trying to learn these principles on their own. Readers with a basic knowledge of linear algebra and probability theory will find it easy to follow. Many excellent books in this field have been published in the past. However, these books are not necessarily intended for self-study by beginners. This book limits the topics to the minimum essential themes that beginners should learn, and explains them in detail. This book focuses on supervised learning, first introducing classical but important methods that have contributed to the development of the field. It then explains various methods that have since attracted attention. In explaining these methods, the book also provides a historical account of how new technologies were created as a result of combining classical ideas. The book emphasizes that Bayes decision rule is a fundamental concept in PR and ML. The following points make this book suitable for self-study by beginners. (1) The book is self-contained, so that the reader does not need to refer to other books or literature. (2) To deepen the reader's understanding, exercises are provided at the end of each chapter with detailed solutions available online. (3) To promote the reader's intuitive understanding, the book presents as many concrete examples as possible. (4) 'Coffee Break' columns introduce knowledge and know-how from the author's experience. Unsupervised learning will be discussed in a sequel.

Contents:

Part I Linear Classification

Chapter 1 Basic Concepts of Pattern Recognition

Chapter 2 Linear Discriminant Functions and their Learning

Chapter 3 Learning based on Minimum Squared Error Criterion

Chapter 4 Classifier Design.-Chapter 5 Feature Evaluation and Bayes Error

Chapter 6 Transformation of Feature Space

Part II Nonlinear Classification

Chapter 7 Subspace Method

Chapter 8 Generalized Linear Discriminant Functions

Chapter 9 Potential Function Method

Chapter 10 Support Vector Machines. Chapter 11 Kernel Methods

Chapter 12 Neural Networks

Part III Bayesian Unified Framework

Chapter 13 Convolutional Neural Networks

Chapter 14 Generalization of Learning Algorithms

Chapter 15 Learning Algorithms and Bayes Decision Rule

Notes:

Includes bibliographical references and index

Online resource; title from PDF title page (SpringerLink, viewed June 26, 2026)

Other Format:

Print version Ishii, Kenichiro Pattern Recognition and Machine Learning for Self-Study I

ISBN:

9789819514786

9819514789

OCLC:

1600225479

Access Restriction:

Restricted for use by site license