LEADER 03341nam 2200613Ia 450 001 9910438050403321 005 20200520144314.0 010 $a3-642-28699-2 024 7 $a10.1007/978-3-642-28699-5 035 $a(CKB)2560000000090927 035 $a(EBL)973078 035 $a(OCoLC)809767619 035 $a(SSID)ssj0000740862 035 $a(PQKBManifestationID)11411171 035 $a(PQKBTitleCode)TC0000740862 035 $a(PQKBWorkID)10720499 035 $a(PQKB)11610505 035 $a(DE-He213)978-3-642-28699-5 035 $a(MiAaPQ)EBC973078 035 $a(PPN)168312581 035 $a(EXLCZ)992560000000090927 100 $a20120904h20122013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aEmerging paradigms in machine learning and applications /$fSheela Ramanna, Lakhmi C. Jain, Robert J. Howlett (eds.) 205 $a1st ed. 2013. 210 $aBerlin ;$aNew York $cSpringer$d2012, c2013 215 $a1 online resource (506 p.) 225 0 $aSmart innovation, systems and technologies,$x2190-3026 ;$v13 300 $aDescription based upon print version of record. 311 $a3-642-43574-2 311 $a3-642-28698-4 320 $aIncludes bibliographical references and index. 327 $aFrom 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. 330 $aThis  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.   . 410 0$aSmart Innovation, Systems and Technologies,$x2190-3018 ;$v13 606 $aMachine learning 606 $aArtificial intelligence 615 0$aMachine learning. 615 0$aArtificial intelligence. 676 $a500 701 $aRamanna$b Sheela$01757400 701 $aJain$b L. C$01601441 701 $aHowlett$b Robert J$0867491 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438050403321 996 $aEmerging paradigms in machine learning and applications$94195233 997 $aUNINA