03737nam 22006255 450 991043805040332120200704045620.03-642-28699-210.1007/978-3-642-28699-5(CKB)2560000000090927(EBL)973078(OCoLC)809767619(SSID)ssj0000740862(PQKBManifestationID)11411171(PQKBTitleCode)TC0000740862(PQKBWorkID)10720499(PQKB)11610505(DE-He213)978-3-642-28699-5(MiAaPQ)EBC973078(PPN)168312581(EXLCZ)99256000000009092720120730d2013 u| 0engur|n|---|||||txtccrEmerging Paradigms in Machine Learning /edited by Sheela Ramanna, Lakhmi C Jain, Robert J. Howlett1st ed. 2013.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2013.1 online resource (506 p.)Smart Innovation, Systems and Technologies,2190-3018 ;13Description based upon print version of record.3-642-43574-2 3-642-28698-4 Includes bibliographical references and index.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.   .Smart Innovation, Systems and Technologies,2190-3018 ;13Computational intelligenceArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.500Ramanna Sheelaedthttp://id.loc.gov/vocabulary/relators/edtJain Lakhmi Cedthttp://id.loc.gov/vocabulary/relators/edtHowlett Robert Jedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910438050403321Emerging Paradigms in Machine Learning2501437UNINA