000			04616nam a22005055i 4500
999			_c12382 _d12382
001			978-3-319-47194-5
003			DE-He213
005			20211206140646.0
008			161026s2017 gw | s |||| 0|eng d
020			_a9783319471945
040			_cAIKTC-KRRC
041			_aENG
072		7	_aUYQ _2bicssc
072		7	_aTEC009000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
100	1		_aSotiropoulos, Dionisios N. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aMachine Learning Paradigms _h[electronic resource] : _bArtificial Immune Systems and their Applications in Software Personalization /
250			_a1st ed. 2017.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017.
300			_aXVI, 327 p. 71 illus., 18 illus. in color. _bCard Paper
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aIntelligent Systems Reference Library, _x1868-4394 ; _v118
520			_aThe topic of this monograph falls within the, so-called, biologically motivated computing paradigm, in which biology provides the source of models and inspiration towards the development of computational intelligence and machine learning systems. Specifically, artificial immune systems are presented as a valid metaphor towards the creation of abstract and high level representations of biological components or functions that lay the foundations for an alternative machine learning paradigm. Therefore, focus is given on addressing the primary problems of Pattern Recognition by developing Artificial Immune System-based machine learning algorithms for the problems of Clustering, Classification and One-Class Classification. Pattern Classification, in particular, is studied within the context of the Class Imbalance Problem. The main source of inspiration stems from the fact that the Adaptive Immune System constitutes one of the most sophisticated biological systems that is exceptionally evolved in order to continuously address an extremely unbalanced pattern classification problem, namely, the self / non-self discrimination process. The experimental results presented in this monograph involve a wide range of degenerate binary classification problems where the minority class of interest is to be recognized against the vast volume of the majority class of negative patterns. In this context, Artificial Immune Systems are utilized for the development of personalized software as the core mechanism behind the implementation of Recommender Systems. The book will be useful to researchers, practitioners and graduate students dealing with Pattern Recognition and Machine Learning and their applications in Personalized Software and Recommender Systems. It is intended for both the expert/researcher in these fields, as well as for the general reader in the field of Computational Intelligence and, more generally, Computer Science who wishes to learn more about the field of Intelligent Computing Systems and its applications. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.
650		0	_aComputer Engineering _94622
653			_aComputational Intelligence.
653			_aArtificial Intelligence.
700	1		_aTsihrintzis, George A. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783319471921
776	0	8	_iPrinted edition: _z9783319471938
776	0	8	_iPrinted edition: _z9783319836751
830		0	_aIntelligent Systems Reference Library, _x1868-4394 ; _v118
856	4	0	_uhttps://doi.org/10.1007/978-3-319-47194-5 _zClick here to access eBook in Springer Nature platform. (Within Campus only.)
942			_cEBOOKS _2ddc