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020 _a9789813299900
024 7 _a10.1007/978-981-32-9990-0
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQ325.5
_b2020 EB
245 0 0 _aEvolutionary Machine Learning Techniques :
_bAlgorithms and Applications
_cedited by Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah
250 _a1st ed. 2020.
264 1 _aSingapore
_bSpringer Singapore :
_bImprint: Springer
_c2020.
300 _a1 recurso en línea (X, 286 páginas)
_b 72 ilustraciones, 55 ilustraciones a color.
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
347 _atext file
_bPDF
490 0 _aAlgorithms for Intelligent Systems
_x2524-7565
490 0 _aIntelligent Technologies and Robotics (Springer-42732)
520 3 _aThis book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields.
988 _aPrimersemestre_2020_Robotics
650 7 _2embne
_aAprendizaje automático
_9166090
700 1 _aMirjalili, Seyedali.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9671160
700 1 _aFaris, Hossam.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aAljarah, Ibrahim.
_eeditor
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
_9106996
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9789813299894
776 0 8 _iPrinted edition:
_z9789813299917
776 0 8 _iPrinted edition:
_z9789813299924
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-32-9990-0
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
_n0
998 _b03/2020
_dz
_ek
_zSI