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020			_a3319471945 _q(electronic book)
020			_a9783319471945 _q(electronic book)
020			_z3319471929 _q(cloth)
020			_z9783319471921 _q(cloth)
035			_a(OCoLC)961438144 _z(OCoLC)962018225 _z(OCoLC)964358553 _z(OCoLC)965479424 _z(OCoLC)965738867 _z(OCoLC)966104438 _z(OCoLC)974650854 _z(OCoLC)1005758244
040			_aEBLCP _cEBLCP _dOCLCO _dN$T _dIDEBK _dEBLCP _dGW5XE _dOCLCQ _dYDX _dOCLCF _dUAB _dWAU _dCOO _dAZU _dMERUC _dUPM _dIOG _dESU _dJBG _dIAD _dICW _dICN _dOCLCQ _dVT2 _dU3W _dES-MaUEC _bspa
050		4	_aQ342 _b.S685 2017 EB
100	1		_aSotiropoulos, Dionisios N., _eautor
245	1	0	_aMachine learning paradigms : _bartificial immune systems and their applications in software personalization _cDionisios N. Sotiropoulos, George A. Tsihrintzis.
264		1	_aCham, Switzerland _bSpringer _c[2017]
264		4	_c2017
300			_a1 recurso en línea (327 páginas) _bilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aIntelligent systems reference library _vvolume 118
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
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	3		_a"The 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"--Provided by publisher.
650		7	_aInmunología _xTécnica _2embne _0(OCoLC)fst00967933 _0 _9676880
700	1		_aTsihrintzis, George A., _eautor _9100283
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-47194-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017A
998			_b02/2018 _dz _e- _zSI
999			_c94785 _d94785 _x1