ITexLi | 2017 | ISBN: 9533070331 9789533070339 | 446 pages | PDF | 24 MB
               
This book presents today’s state and development tendencies of 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 book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning.

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition.

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 to machine learning. The author also attempts to promote a new design of thinking machines 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, and they 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 and RNA target prediction.

Contents
Preface
1  Machine Learning: When and                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      Where the Horses Went Astray?
2  SOMs for Machine Learning
3  Relational Analysis for Clustering Consensus
4  A Classifier Fusion System with Verification Module for Improving Recognition Reliability
5  Watermarking Representation for Adaptive Image Classification with Radial Basis Function Network
6  Recent Advances in Neural Networks Structural Risk Minimization Based on Multiobjective Complexity Control Algorithms
7  Statistics Character and Complexity in Nonlinear Systems
8  Adaptive Basis Function Construction: An Approach for Adaptive Building of Sparse Polynomial Regression Models
9  On The Combination of Feature and Instance Selection
10  Fuzzy System with Positive and Negative Rules
11  Automatic Construction of Knowledge-Based System Using Knowware System
12  Applying Fuzzy Bayesian Maximum Entropy to Extrapolating Deterioration in Repairable Systems
13  Alarming Large Scale of Flight Delays: an Application of Machine Learning
14  Machine Learning Tools for Geomorphic Mapping of Planetary Surfaces
15  Network Intrusion Detection using Machine Learning and Voting techniques
16  Artificial Immune Network: Classification on Heterogeneous Data
17  Modified Cascade Correlation Neural Network and its Applications to Multidisciplinary Analysis Design and Optimization in Ship Design
18  Massive-Training Artificial Neural Networks (MTANN) in Computer-Aided Detection of Colorectal Polyps and Lung Nodules in CT
19  Automated Detection and Analysis of Particle Beams in Laser-Plasman Accelerator Simulations
20  Specificity Enhancement in microRNA Target Prediction through Knowledge Discovery
21  Extraction Of Meaningful Rules In A Medical Database
22  Establishing and Retrieving Domain Knowledge from Semi-Structural Corpora