Zhang Y. (ed.) Machine Learning

InTech, 2010, -446 p.The goal of this book is to present the key algorithms, theory and applications that from the core of machine learning. Learning is a fundamental activity. It is the process of constructing a model from complex world. And it is also the prerequisite for the performance of any new activity and, later, for the improvement in this performance. Machine learning is concerned with constructing computer programs that automatically improve with experience. It draws on concepts and results from many fields, including artificial intelligence, statistics, control theory, cognitive science, information theory, etc. The field of machine learning is developing rapidly both in theory and applications in recent years, and machine learning has been applied to successfully solve a lot of real-world problems.
Machine learning theory attempts to answer questions such as How does learning performance vary with the number of training examples presented? and Which learning algorithms are most appropriate for various types of learning tasks? Machine learning methods are extremely useful in recognizing patterns in large datasets and making predictions based on these patterns when presented with new data. A variety of machine learning methods have been developed since the emergence of artificial intelligence research in the early 20th century. These methods involve the application of one or more automated algorithms to a body of data. There are various methods developed to evaluate the effectiveness of machine learning methods, and those methods can be easily extended to evaluate the utility of different machine learning attributes as well.
Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the Introduction of machine learning. The author also attempts to promote a new thinking machines design and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS).Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT, RNA target prediction, 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 basic approaches of machine learning, and it is also the source of useful bibliographical information.Part I Introduction
Machine Learning: When and Where the Horses Went Astray?
Part II Learning Theory
SOMs for machine learning
Relational Analysis for Clustering Consensus
A Classifier Fusion System with Verification Module for Improving Recognition Reliability
Watermarking Representation for Adaptive Image Classification with Radial Basis Function Network
Recent advances in Neural Networks Structural Risk Minimization based on multiobjective complexity control algorithms
Statistics Character and Complexity in Nonlinear Systems
Adaptive Basis Function Construction: An Approach for Adaptive Building of Sparse Polynomial Regression Models
On the Combination of Feature and Instance Selection
Fuzzy System with Positive and Negative Rules
Automatic Construction of Knowledge-Based System using Knowware System
Applying Fuzzy Bayesian Maximum Entropy to Extrapolating Deterioration in Repairable Systems
Part III Applications
Alarming Large Scale of Flight Delays: an Application of Machine Learning
Machine Learning Tools for Geomorphic Mapping of Planetary Surfaces
Network Intrusion Detection using Machine Learning and Voting techniques
Artificial Immune Network: Classification on Heterogeneous Data
Modified Cascade Correlation Neural Network and its Applications to Multidisciplinary Analysis Design and Optimization in Ship Design
Massive-Training Artificial Neural Networks (MTANN) in Computer-Aided Detection of Colorectal Polyps and Lung Nodules in CT
Automated detection and analysis of particle beams in laser-plasma accelerator simulations
Specificity Enhancement in microRNA Target Prediction through Knowledge Discovery
Extraction Of Meaningful Rules in a Medical Database
Establishing and retrieving domain knowledge from semi-structural corpora