000 03127nam a22004935i 4500
001 978-3-642-28699-5
003 DE-He213
005 20200421111844.0
007 cr nn 008mamaa
008 120730s2013 gw | s |||| 0|eng d
020 _a9783642286995
_9978-3-642-28699-5
024 7 _a10.1007/978-3-642-28699-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aEmerging Paradigms in Machine Learning
_h[electronic resource] /
_cedited by Sheela Ramanna, Lakhmi C Jain, Robert J. Howlett.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXXII, 498 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSmart Innovation, Systems and Technologies,
_x2190-3018 ;
_v13
505 0 _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.
520 _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.   .
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aRamanna, Sheela.
_eeditor.
700 1 _aJain, Lakhmi C.
_eeditor.
700 1 _aHowlett, Robert J.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642286988
830 0 _aSmart Innovation, Systems and Technologies,
_x2190-3018 ;
_v13
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-28699-5
912 _aZDB-2-ENG
942 _cEBK
999 _c55734
_d55734