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Emerging Paradigms in Machine Learning

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Cover of 'Emerging Paradigms in Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Emerging Paradigms in Machine Learning
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    Chapter 2 Extensions of Dynamic Programming as a New Tool for Decision Tree Optimization
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    Chapter 3 Optimised Information Abstraction in Granular Min/Max Clustering
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    Chapter 4 Mining Incomplete Data—A Rough Set Approach
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    Chapter 5 Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigation
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    Chapter 6 Evolving Intelligent Systems: Methods, Algorithms and Applications
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    Chapter 7 Emerging Trends in Machine Learning: Classification of Stochastically Episodic Events
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    Chapter 8 Learning of Defaults by Agents in a Distributed Multi-Agent System Environment
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    Chapter 9 Rough Non-deterministic Information Analysis: Foundations and Its Perspective in Machine Learning
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    Chapter 10 Introduction to Perception Based Computing
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    Chapter 11 Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data
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    Chapter 12 A Granular Computing Paradigm for Concept Learning
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    Chapter 13 Emerging Paradigms in Machine Learning
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    Chapter 14 The Mamdani Expert-System with Parametric Families of Fuzzy Constraints in Evaluation of Cancer Patient Survival Length
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    Chapter 15 Support Vector Machines in Biomedical and Biometrical Applications
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    Chapter 16 Workload Modeling for Multimedia Surveillance Systems
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    Chapter 17 Rough Set and Artificial Neural Network Approach to Computational Stylistics
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    Chapter 18 Application of Learning Algorithms to Image Spam Evolution
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Title
Emerging Paradigms in Machine Learning
Published by
Springer-Verlag Berlin Heidelberg, January 2013
DOI 10.1007/978-3-642-28699-5
ISBNs
978-3-64-228698-8, 978-3-64-228699-5, 978-3-64-243574-4
Editors

Sheela Ramanna, Lakhmi C. Jain, Robert J. Howlett

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 40%
Unspecified 1 20%
Student > Ph. D. Student 1 20%
Unknown 1 20%
Readers by discipline Count As %
Computer Science 2 40%
Unspecified 1 20%
Engineering 1 20%
Unknown 1 20%