LEADER 03779nam 22005775 450 001 9910483998803321 005 20200701134329.0 010 $a981-329-990-8 024 7 $a10.1007/978-981-32-9990-0 035 $a(CKB)4100000009837050 035 $a(MiAaPQ)EBC5976183 035 $a(DE-He213)978-981-32-9990-0 035 $a(PPN)243768206 035 $a(EXLCZ)994100000009837050 100 $a20191111d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEvolutionary Machine Learning Techniques $eAlgorithms and Applications /$fedited by Seyedali Mirjalili, Hossam Faris, Ibrahim Aljarah 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (287 pages) 225 1 $aAlgorithms for Intelligent Systems,$x2524-7565 311 $a981-329-989-4 330 $aThis book provides an in-depth analysis of the current evolutionary machine learning techniques. Discussing the most highly regarded methods for classification, clustering, regression, and prediction, it includes techniques such as support vector machines, extreme learning machines, evolutionary feature selection, artificial neural networks including feed-forward neural networks, multi-layer perceptron, probabilistic neural networks, self-optimizing neural networks, radial basis function networks, recurrent neural networks, spiking neural networks, neuro-fuzzy networks, modular neural networks, physical neural networks, and deep neural networks. The book provides essential definitions, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons of classical training algorithms. It features a range of proven and recent nature-inspired algorithms used to train different types of artificial neural networks, including genetic algorithm, ant colony optimization, particle swarm optimization, grey wolf optimizer, whale optimization algorithm, ant lion optimizer, moth flame algorithm, dragonfly algorithm, salp swarm algorithm, multi-verse optimizer, and sine cosine algorithm. The book also covers applications of the improved artificial neural networks to solve classification, clustering, prediction and regression problems in diverse fields. 410 0$aAlgorithms for Intelligent Systems,$x2524-7565 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aNeural networks (Computer science)  606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aNeural networks (Computer science) . 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 676 $a006.31 702 $aMirjalili$b Seyedali$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aFaris$b Hossam$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAljarah$b Ibrahim$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483998803321 996 $aEvolutionary Machine Learning Techniques$91933714 997 $aUNINA