Screen LibraryEmerging paradigms in machine learning /
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 stu...
Saved in:
| Corporate Authors: | |
|---|---|
| Group Author: | ; ; |
| Published: |
Springer,
|
| Publisher Address: | Berlin ; New York : |
| Publication Dates: | 2013. |
| Literature type: | eBook |
| Language: | English |
| Series: |
Smart innovation, systems and technologies,
13 |
| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-3-642-28699-5 |
| Summary: |
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. |
| Carrier Form: | 1 online resource. |
| Bibliography: | Includes bibliographical references and author index. |
| ISBN: |
9783642286995 (electronic bk.) 3642286992 (electronic bk.) |
| Index Number: | Q325 |
| CLC: | TP181 |
| Contents: |
FOUNDATIONS -- Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization / Optimised Information Abstraction in Granular Min/Max Clustering / Mining Incomplete Data--A Rough Set Approach / Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation / Evolving Intelligent Systems: Methods, Algorithms and Applications / Emerging Trends in Machine Learning: Classification of Stochastically Episodic Events / Learning of Defaults by Agents in a Distributed Multi-Agent System Environment / Rough Non-deterministic Information Analysis: Foundations and Its Perspective in Machine Learning / Introduction to Perception Based Computing / Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data / A Granular Computing Paradigm for Concept Learning / Emerging Paradigms in Machine Learning: An Introduction / APPLICATIONS -- Identifying Calendar-Based Periodic Patterns / The Mamdani Expert-System with Parametric Families of Fuzzy Constraints in Evaluation of Cancer Patient Survival Length / Support Vector Machines in Biomedical and Biometrical Applications / Workload Modeling for Multimedia Surveillance Systems / Rough Set and Artificial Neural Network Approach to Computational Stylistics / Application of Learning Algorithms to Image Spam Evolution / |