LEADER 05436oam 22007454a 450 001 9910777796103321 005 20190503073336.0 010 $a0-262-29279-3 010 $a9786612096372 010 $a1-282-09637-0 010 $a0-262-25631-2 010 $a1-4294-1873-7 035 $a(CKB)1000000000468192 035 $a(EBL)3338650 035 $a(SSID)ssj0000210212 035 $a(PQKBManifestationID)11189860 035 $a(PQKBTitleCode)TC0000210212 035 $a(PQKBWorkID)10268137 035 $a(PQKB)10764460 035 $a(CaBNVSL)mat06267276 035 $a(IDAMS)0b000064818b4262 035 $a(IEEE)6267276 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 035 $a(OCoLC-P)77521428 035 $a(MaCbMITP)4977 035 $a(Au-PeEL)EBL3338650 035 $a(CaPaEBR)ebr10173712 035 $a(OCoLC)77521428 035 $a(MiAaPQ)EBC3338650 035 $a(PPN)170238210 035 $a(EXLCZ)991000000000468192 100 $a20070103d2007 uy 0 101 0 $aeng 135 $aur|n||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aNew directions in statistical signal processing $efrom systems to brain /$fedited by Simon Haykin [and others] 210 $aCambridge, Mass. $cMIT Press$dİ2007 215 $a1 online resource (544 p.) 225 1 $aNeural information processing series 300 $aDescription based upon print version of record. 311 $a0-262-08348-5 320 $aIncludes bibliographical references (p. [465]-508) and index. 327 $tModeling the mind : from circuits to systems /$rSuzanna Becker --$tEmpirical statistics and stochastic models for visual signals /$rDavid Mumford --$gThe$tmachine cocktail party problem /$rSimon Haykin, Zhe Chen --$tSensor adaptive signal processing of biological nanotubes (ion channels) at macroscopic and nano scales /$rVikram Krishnamurthy --$tSpin diffusion : a new perspective in magnetic resonance imaging /$rTimothy R. Field --$tWhat makes a dynamical system computationally powerful? /$rRobert Legenstein, Wolfgang Maass --$gA$tvariational principle for graphical models /$rMartin J. Wainwright, Michael I. Jordan --$tModeling large dynamical systems with dynamical consistent neural networks /$rHans-Georg Zimmermann ... [et al.] --$tDiversity in communication : from source coding to wireless networks /$rSuhas N. Diggavi --$tDesigning patterns for easy recognition : information transmission with low-density parity-check codes /$rFrank R. Kschischang, Masoud Ardakani --$tTurbo processing /$rClaude Berrou, Charlotte Langlais, Fabrice Seguin --$tBlind signal processing based on data geometric properties /$rKonstantinos Diamantaras --$tGame-theoretic learning /$rGeoffrey J. Gordon --$tLearning observable operator models via the efficient sharpening algorithm /$rHerbert Jaeger ... [et al.]. 330 $aSignal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines. The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs). 410 0$aNeural information processing series 606 $aNeural networks (Neurobiology) 606 $aNeural networks (Computer science) 606 $aSignal processing$xStatistical methods 606 $aNeural computers 610 $aCOMPUTER SCIENCE/Machine Learning & Neural Networks 610 $aNEUROSCIENCE/General 615 0$aNeural networks (Neurobiology) 615 0$aNeural networks (Computer science) 615 0$aSignal processing$xStatistical methods. 615 0$aNeural computers. 676 $a612.8/2 701 $aHaykin$b Simon S.$f1931-$08857 801 0$bOCoLC-P 801 1$bOCoLC-P 906 $aBOOK 912 $a9910777796103321 996 $aNew directions in statistical signal processing$93821321 997 $aUNINA