Zhang Y. (ed.) New Advances in Machine Learning

InTech, 2010, — 374 p.The purpose of this book is to provide an up-to-data and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call Learning 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 learning 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 learners based upon empirical data. Machine learning 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 learning 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 learning.
Large amount of knowledge about machine learning 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 learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, 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 learning, and it is also the source of useful bibliographical information.Introduction to Machine Learning
Machine Learning Overview
Types of Machine Learning Algorithms
Methods for Pattern 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 Learning for Multi-stage Selection of Numerical Methods
Hierarchical Reinforcement Learning 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 Learning 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 learning 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 Learning for Sentence Understanding
A Hebbian Learning Approach for Diffusion Tensor Analysis & Tractography
A Novel Credit Assignment to a Rule with Probabilistic State Transition