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020 _a9783319471945
_9978-3-319-47194-5
024 7 _a10.1007/978-3-319-47194-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
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082 0 4 _a006.3
_223
100 1 _aSotiropoulos, Dionisios N.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_962411
245 1 0 _aMachine Learning Paradigms
_h[electronic resource] :
_bArtificial Immune Systems and their Applications in Software Personalization /
_cby Dionisios N. Sotiropoulos, George A. Tsihrintzis.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXVI, 327 p. 71 illus., 18 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4408 ;
_v118
505 0 _aIntroduction -- Machine Learning -- The Class Imbalance Problem -- Addressing the Class Imbalance Problem -- Machine Learning Paradigms -- Immune System Fundamentals -- Artificial Immune Systems -- Experimental Evaluation of Artificial Immune System-based Learning Algorithms -- Conclusions and Future Work.
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 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aTsihrintzis, George A.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_962412
710 2 _aSpringerLink (Online service)
_962413
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,
_x1868-4408 ;
_v118
_962414
856 4 0 _uhttps://doi.org/10.1007/978-3-319-47194-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80966
_d80966