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Emerging Paradigms in Machine Learning: Smart Innovation, Systems and Technologies, cartea 13

Editat de Sheela Ramanna, Lakhmi C Jain, Robert J. Howlett
en Limba Engleză Paperback – 9 aug 2014
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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Specificații

ISBN-13: 9783642435744
ISBN-10: 3642435742
Pagini: 520
Ilustrații: XXII, 498 p.
Dimensiuni: 155 x 235 x 27 mm
Greutate: 0.72 kg
Ediția:2013
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Smart Innovation, Systems and Technologies

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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.

Textul de pe ultima copertă

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.   

Caracteristici

State of the art of emerging paradigms in machine learning including some real world applications Latest research in machine learning and biologically-based techniques for the design and implementation of intelligent systems Written by leading experts in the field