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New Advances in Machine Learning
Edited by Yagang Zhang, ISBN 978-953-307-034-6, Hard cover, 366 pages, Publisher: InTech, Published: February 01, 2010 under CC BY-NC-SA 3.0 license, in subject Artificial Intelligence
DOI: 10.5772/225
The purpose of this book is to provide an up-to-date 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 computers that improve from experience in quite straightforward ways. 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 a good introduction to many approaches of machine learning, and it is also the source of useful bibliographical information.
This book is indexed in:
Book contents
- Chapter 1Introduction to Machine Learning
- Chapter 2Machine Learning Overview
- Chapter 3Types of Machine Learning Algorithms
- Chapter 4Methods for Pattern Classification
- Chapter 5Classification of Support Vector Machine and Regression Algorithm
- Chapter 6Classifiers Association for High Dimensional Problem: Application to Pedestrian Recognition
- Chapter 7From Feature Space to Primal Space: KPCA and Its Mixture Model
- Chapter 8Machine Learning for Multi-stage Selection of Numerical Methods
- Chapter 9Hierarchical Reinforcement Learning Using a Modular Fuzzy Model for Multi-Agent Problem
- Chapter 10Random Forest-LNS Architecture and Vision
- Chapter 11An Intelligent System for Container Image Recognition using ART2-based Self-Organizing Supervised Learning Algorithm
- Chapter 12Data Mining with Skewed Data
- Chapter 13Scaling up Instance Selection Algorithms by Dividing-and-Conquering
- Chapter 14Ant Colony Optimization
- Chapter 15Mahalanobis Support Vector Machines Made Fast and Robust
- Chapter 16On-line Learning of Fuzzy Rule Emulated Networks for a Class of Unknown Nonlinear Discrete-Time Controllers with Estimated Linearization
- Chapter 17Knowledge Structures for Visualising Advanced Research and Trends
- Chapter 18Dynamic Visual Motion Estimation
- Chapter 19Concept Mining and Inner Relationship Discovery from Text
- Chapter 20Cognitive Learning for Sentence Understanding
- Chapter 21A Hebbian Learning Approach for Diffusion Tensor Analysis and Tractography
- Chapter 22A Novel Credit Assignment to a Rule with Probabilistic State Transition