01141nam 2200385 450 99000337809020331620100310130150.0978-0-520-07740-9000337809USA01000337809(ALEPH)000337809USA0100033780920100310d1992----km-y0itay50------baengUS||||||||001yyBeyond recognitionrepresentation, power and cultureCraig Owensedited by Scott Bryson...[et al.]introduction by Simon WatneyBerkeley [etc.]University of California1992XVII, 386 p.24 cm20012001ArteSec. 20.BNCF709.04OWENS,Craig607200BRYSON,ScottWATNEY,SimonITsalbcISBD990003378090203316aaa218846 L.M.aaa00256478BKUMAALESSANDRA9020100310USA011248ALESSANDRA9020100310USA011301Beyond recognition1122940UNISA00792nam0-22003011i-450-990000877540403321200010100-442-20828-6000087754FED01000087754(Aleph)000087754FED0100008775420001010d--------km-y0itay50------baitay-------001yyFlow-Induced VibrationNew YorkVan Nostrand Reinhold Company1977p.363ill.cm 20Dinamica SismicaBlevins,Robert D.29307ITUNINARICAUNIMARCBK99000087754040332103 D.0,1563963IINTCIINTCFlow induced vibration329317UNINAING0105009nam 22006975 450 991034933600332120200704182329.01-4471-7452-610.1007/978-1-4471-7452-3(CKB)4100000009273666(DE-He213)978-1-4471-7452-3(MiAaPQ)EBC5918939(PPN)269146105(EXLCZ)99410000000927366620190912d2019 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierNeural Networks and Statistical Learning /by Ke-Lin Du, M. N. S. Swamy2nd ed. 2019.London :Springer London :Imprint: Springer,2019.1 online resource (XXX, 988 p. 184 illus., 70 illus. in color.)1-4471-7451-8 Introduction -- Fundamentals of Machine Learning -- Perceptrons -- Multilayer perceptrons: architecture and error backpropagation -- Multilayer perceptrons: other learing techniques -- Hopfield networks, simulated annealing and chaotic neural networks -- Associative memory networks -- Clustering I: Basic clustering models and algorithms -- Clustering II: topics in clustering -- Radial basis function networks -- Recurrent neural networks -- Principal component analysis -- Nonnegative matrix factorization and compressed sensing -- Independent component analysis -- Discriminant analysis -- Support vector machines -- Other kernel methods -- Reinforcement learning -- Probabilistic and Bayesian networks -- Combining multiple learners: data fusion and emsemble learning -- Introduction of fuzzy sets and logic -- Neurofuzzy systems -- Neural circuits -- Pattern recognition for biometrics and bioinformatics -- Data mining.This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models; • clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.Neural networks (Computer science)Computational intelligenceArtificial intelligencePattern perceptionSignal processingImage processingSpeech processing systemsMathematical Models of Cognitive Processes and Neural Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/M13100Computational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XSignal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Neural networks (Computer science)Computational intelligence.Artificial intelligence.Pattern perception.Signal processing.Image processing.Speech processing systems.Mathematical Models of Cognitive Processes and Neural Networks.Computational Intelligence.Artificial Intelligence.Pattern Recognition.Signal, Image and Speech Processing.001.534Du Ke-Linauthttp://id.loc.gov/vocabulary/relators/aut756075Swamy M. N. Sauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910349336003321Neural Networks and Statistical Learning2041918UNINA