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001385653 020__ $$a9780262256315$$q(electronic bk.)
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001385653 020__ $$z0262083485$$q(alk. paper)
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001385653 035__ $$a(OCoLC)77521428$$z(OCoLC)148793201$$z(OCoLC)228169954$$z(OCoLC)228169955$$z(OCoLC)473746968$$z(OCoLC)475448941$$z(OCoLC)568000769$$z(OCoLC)607844062$$z(OCoLC)609208517$$z(OCoLC)722566137$$z(OCoLC)728037360$$z(OCoLC)961519368$$z(OCoLC)962716687$$z(OCoLC)974200821$$z(OCoLC)974437145$$z(OCoLC)982304975$$z(OCoLC)988489573$$z(OCoLC)991913911$$z(OCoLC)992055259$$z(OCoLC)1018006113$$z(OCoLC)1037913547$$z(OCoLC)1038670433$$z(OCoLC)1041494199$$z(OCoLC)1047652407$$z(OCoLC)1053418672$$z(OCoLC)1054119184$$z(OCoLC)1055389940$$z(OCoLC)1066431409$$z(OCoLC)1081204198
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001385653 24500 $$aNew directions in statistical signal processing :$$bfrom systems to brain /$$cedited by Simon Haykin [and others].
001385653 260__ $$aCambridge, Mass. :$$bMIT Press,$$c©2007.
001385653 300__ $$a1 online resource (vi, 514 pages) :$$billustrations.
001385653 336__ $$atext$$btxt$$2rdacontent
001385653 337__ $$acomputer$$bc$$2rdamedia
001385653 338__ $$aonline resource$$bcr$$2rdacarrier
001385653 4901_ $$aNeural information processing series
001385653 506__ $$aAccess limited to authorized users.
001385653 520__ $$aSignal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines. The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs).
001385653 588__ $$aOCLC-licensed vendor bibliographic record.
001385653 650_0 $$aNeural networks (Neurobiology)
001385653 650_0 $$aNeural networks (Computer science)
001385653 650_0 $$aSignal processing$$xStatistical methods.
001385653 650_0 $$aNeural computers.
001385653 653__ $$aCOMPUTER SCIENCE/Machine Learning & Neural Networks
001385653 653__ $$aNEUROSCIENCE/General
001385653 655_0 $$aElectronic books
001385653 7001_ $$aHaykin, Simon S.,$$d1931-
001385653 852__ $$bebk
001385653 85640 $$3MIT Press$$uhttps://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/4977.001.0001?locatt=mode:legacy$$zOnline Access through The MIT Press Direct
001385653 85642 $$3OCLC metadata license agreement$$uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
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001385653 980__ $$aEBOOK
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