Издательство InTech, 2010, -374 pp.
The purpose of this book is to provide an up-to-data and systematical introduction to the principles and algorithms of machine leaing. The definition of leaing is broad enough to include most tasks that we commonly call Leaing tasks, as we use the word in daily life. It is also broad enough to encompass computer that improve from experience in quite straight forward ways.
Machine leaing addresses the question of how to build computer programs that improve their performance at some task through experience. It attempts to automate the estimation process by building machine leaers based upon empirical data. Machine leaing algorithms have been proven to be of great practical value in a variety application domain, such as, data mining problems where large databases may contain valuable implicit regularities that can be discovered automatically; poorly understood domains where humans might not have the knowledge needed to develop effective algorithms; domains where the program must dynamically adapt to changing conditions.
Machine leaing is inherently a multidisciplinary field. It draws on results from artificial intelligence, probability and statistics, computational complexity theory, control theory, information theory, philosophy, psychology, neurobiology, and other fields. The goal of this book is to present the important advances in the theory and algorithm that from the foundations of machine leaing.
Large amount of knowledge about machine leaing has been presented in this book, mainly include: classification, support vector machine, discriminant analysis, multi-agent system, image recognition, ant colony optimization, and so on.
The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine leaing. The book is intended for both graduate and postgraduate students in fields such as computer science, cybeetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners. The wide scope of the book provides them with a good introduction to many approaches of machine leaing, and it is also the source of useful bibliographical information.
Introduction to Machine Leaing
Machine Leaing Overview
Types of Machine Leaing Algorithms
Methods for Patte Classification
Classification of support vector machine and regression algorithm
Classifiers Association for High Dimensional Problem: Application to Pedestrian Recognition
From Feature Space to Primal Space: KPCA and Its Mixture Model
Machine Leaing for Multi-stage Selection of Numerical Methods
Hierarchical Reinforcement Leaing Using a Modular Fuzzy Model for Multi-Agent Problem
Random Forest-LNS Architecture and Vision
An Intelligent System for Container Image Recognition using ART2-based Self-Organizing Supervised Leaing Algorithm
Data mining with skewed data
Scaling up instance selection algorithms by dividing-and-conquering
Ant Colony Optimization
Mahalanobis Support Vector Machines Made Fast and Robust
On-line leaing of fuzzy rule emulated networks for a class of unknown nonlinear discrete-time controllers with estimated linearization
Knowledge Structures for Visualising Advanced Research and Trends
Dynamic Visual Motion Estimation
Concept Mining and Inner Relationship Discovery from Text
Cognitive Leaing for Sentence Understanding
A Hebbian Leaing Approach for Diffusion Tensor Analysis & Tractography
A Novel Credit Assignment to a Rule with Probabilistic State Transition
The purpose of this book is to provide an up-to-data and systematical introduction to the principles and algorithms of machine leaing. The definition of leaing is broad enough to include most tasks that we commonly call Leaing tasks, as we use the word in daily life. It is also broad enough to encompass computer that improve from experience in quite straight forward ways.
Machine leaing addresses the question of how to build computer programs that improve their performance at some task through experience. It attempts to automate the estimation process by building machine leaers based upon empirical data. Machine leaing algorithms have been proven to be of great practical value in a variety application domain, such as, data mining problems where large databases may contain valuable implicit regularities that can be discovered automatically; poorly understood domains where humans might not have the knowledge needed to develop effective algorithms; domains where the program must dynamically adapt to changing conditions.
Machine leaing is inherently a multidisciplinary field. It draws on results from artificial intelligence, probability and statistics, computational complexity theory, control theory, information theory, philosophy, psychology, neurobiology, and other fields. The goal of this book is to present the important advances in the theory and algorithm that from the foundations of machine leaing.
Large amount of knowledge about machine leaing has been presented in this book, mainly include: classification, support vector machine, discriminant analysis, multi-agent system, image recognition, ant colony optimization, and so on.
The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine leaing. The book is intended for both graduate and postgraduate students in fields such as computer science, cybeetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners. The wide scope of the book provides them with a good introduction to many approaches of machine leaing, and it is also the source of useful bibliographical information.
Introduction to Machine Leaing
Machine Leaing Overview
Types of Machine Leaing Algorithms
Methods for Patte Classification
Classification of support vector machine and regression algorithm
Classifiers Association for High Dimensional Problem: Application to Pedestrian Recognition
From Feature Space to Primal Space: KPCA and Its Mixture Model
Machine Leaing for Multi-stage Selection of Numerical Methods
Hierarchical Reinforcement Leaing Using a Modular Fuzzy Model for Multi-Agent Problem
Random Forest-LNS Architecture and Vision
An Intelligent System for Container Image Recognition using ART2-based Self-Organizing Supervised Leaing Algorithm
Data mining with skewed data
Scaling up instance selection algorithms by dividing-and-conquering
Ant Colony Optimization
Mahalanobis Support Vector Machines Made Fast and Robust
On-line leaing of fuzzy rule emulated networks for a class of unknown nonlinear discrete-time controllers with estimated linearization
Knowledge Structures for Visualising Advanced Research and Trends
Dynamic Visual Motion Estimation
Concept Mining and Inner Relationship Discovery from Text
Cognitive Leaing for Sentence Understanding
A Hebbian Leaing Approach for Diffusion Tensor Analysis & Tractography
A Novel Credit Assignment to a Rule with Probabilistic State Transition