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020			_a9783030023843
024	7		_a10.1007/978-3-030-02384-3 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQ325.5 _b2019 EB
100	1		_aAngelov, Plamen _eautor _997963
245	1	0	_aEmpirical Approach to Machine Learning _cby Plamen P. Angelov, Xiaowei Gu.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (XXIX, 423 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aStudies in Computational Intelligence _x1860-949X ; _v800
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction -- Part I: Theoretical Background -- Brief Introduction to Statistical Machine Learning -- Brief Introduction to Computational Intelligence -- Part II: Theoretical Fundamentals of the Proposed Approach -- Empirical Approach - Introduction -- Empirical Fuzzy Sets and Systems -- Anomaly Detection - Empirical Approach -- Data Partitioning - Empirical Approach -- Autonomous Learning Multi-Model Systems -- Transparent Deep Rule-Based Classifiers -- Part III: Applications of the Proposed Approach -- Applications of Autonomous Anomaly Detection.
520	3		_aThis book provides a 'one-stop source' for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today's data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code. Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA: "The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing." Paul J. Werbos, Inventor of the back-propagation method, USA: "I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain." Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: "This new book will set up a milestone for the modern intelligent systems." Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: "Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.".
650		7	_2embne _aAprendizaje automático _9166090
700	1		_aGu, Xiaowei. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030023836
776	0	8	_iPrinted edition: _z9783030023850
776	0	8	_iPrinted edition: _z9783030132095
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-02384-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Robotics
998			_aSI _cm _dz _feng _ggw _h0 _b11/2019 _ek _zSI