Emerging Paradigms in Machine Learning

✍ Scribed by Sheela Ramanna, Lakhmi C. Jain, Robert J. Howlett (auth.), Sheela Ramanna, Lakhmi C Jain, Robert J. Howlett (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2013
• Tongue: English
• Leaves: 506
• Series: Smart Innovation, Systems and Technologies 13
• Edition: 1
• Category: Library

✦ Synopsis

This book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The multidisciplinary nature of machine learning makes it a very fascinating and popular area for research. The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems. Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.

✦ Table of Contents

Front Matter....Pages 1-20
Emerging Paradigms in Machine Learning: An Introduction....Pages 1-8
Front Matter....Pages 9-9
Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization....Pages 11-29
Optimised Information Abstraction in Granular Min/Max Clustering....Pages 31-48
Mining Incomplete Data—A Rough Set Approach....Pages 49-74
Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation....Pages 75-116
Evolving Intelligent Systems: Methods, Algorithms and Applications....Pages 117-159
Emerging Trends in Machine Learning: Classification of Stochastically Episodic Events....Pages 161-195
Learning of Defaults by Agents in a Distributed Multi-Agent System Environment....Pages 197-213
Rough Non-deterministic Information Analysis: Foundations and Its Perspective in Machine Learning....Pages 215-247
Introduction to Perception Based Computing....Pages 249-275
Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data....Pages 277-306
A Granular Computing Paradigm for Concept Learning....Pages 307-326
Front Matter....Pages 327-327
Identifying Calendar-Based Periodic Patterns....Pages 329-357
The Mamdani Expert-System with Parametric Families of Fuzzy Constraints in Evaluation of Cancer Patient Survival Length....Pages 359-378
Support Vector Machines in Biomedical and Biometrical Applications....Pages 379-417
Workload Modeling for Multimedia Surveillance Systems....Pages 419-440
Rough Set and Artificial Neural Network Approach to Computational Stylistics....Pages 441-470
Application of Learning Algorithms to Image Spam Evolution....Pages 471-495
Back Matter....Pages 0--1

✦ Subjects

Computational Intelligence; Artificial Intelligence (incl. Robotics)