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Emerging Paradigms in Machine Learning [electronic resource] / edited by Sheela Ramanna, Lakhmi C Jain, Robert J. Howlett.

Contributor(s): Ramanna, Sheela [editor.] | Jain, Lakhmi C [editor.] | Howlett, Robert J [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Smart Innovation, Systems and Technologies: 13Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XXII, 498 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642286995.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
From the content: Emerging Paradigms in Machine Learning: An Introduction -- 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.
In: Springer eBooksSummary: 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.   .
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From the content: Emerging Paradigms in Machine Learning: An Introduction -- 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.

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.   .

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