LEADER 05335nam 22007094a 450 001 9911020031603321 005 20200520144314.0 010 $a9786610198559 010 $a9781405143875 010 $a1405143878 010 $a9781280198557 010 $a1280198559 010 $a9780470694077 010 $a0470694076 010 $a9781405143899 010 $a1405143894 035 $a(CKB)1000000000408324 035 $a(EBL)233132 035 $a(SSID)ssj0000128841 035 $a(PQKBManifestationID)11144220 035 $a(PQKBTitleCode)TC0000128841 035 $a(PQKBWorkID)10072145 035 $a(PQKB)11282251 035 $a(MiAaPQ)EBC233132 035 $a(MiAaPQ)EBC4956761 035 $a(Au-PeEL)EBL4956761 035 $a(CaONFJC)MIL19855 035 $a(OCoLC)214281471 035 $a(Perlego)2756042 035 $a(EXLCZ)991000000000408324 100 $a20041206d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aConnectionism $ea hands-on approach /$fMichael R.W. Dawson 205 $a1st ed. 210 $aOxford, UK ;$aMalden, MA $cBlackwell Pub.$d2005 215 $a1 online resource (210 p.) 300 $aDescription based upon print version of record. 311 08$a9781405130745 311 08$a1405130741 320 $aIncludes bibliographical references (p. [188]-194) and indexes. 327 $aCONNECTIONISM; Contents; Chapter 1:Hands-On Connectionism; 1.1 Connectionism in Principle and in Practice; 1.2 The Organization of This Book; Chapter 2:The Distributed Associative Memory; 2.1 The Paired Associate Task; 2.2 The Standard Pattern Associator; 2.3 Exploring the Distributed Associative Memory; Chapter 3:The James Program; 3.1 Introduction; 3.2 Installing the Program; 3.3 Teaching a Distributed Memory; 3.4 Testing What the Memory Has Learned; 3.5 Using the Program; Chapter 4:Introducing Hebb Learning; 4.1 Overview of the Exercises; 4.2 Hebb Learning of Basis Vectors 327 $a4.3 Hebb Learning of Orthonormal,Non-Basis VectorsAppendix - Creating mutually orthogonal vectors with Maple; Chapter 5:Limitations of Hebb Learning; 5.1 Introduction; 5.2 The Effect of Repetition; 5.3 The Effect of Correlation; Appendix - Creating the linearly independent set of vectors; Chapter 6:Introducing the Delta Rule; 6.1 Introduction; 6.2 The Delta Rule; 6.3 The Delta Rule and the Effect of Repetition; 6.4 The Delta Rule and the Effect of Correlation; Chapter 7:Distributed Networks and Human Memory; 7.1 Background on the Paired Associate Paradigm 327 $a7.2 The Effect of Similarity on the Distributed Associative MemoryChapter 8:Limitations of Delta Rule Learning; 8.1 Introduction; 8.2 The Delta Rule and Linear Dependency; Chapter 9:The Perceptron; 9.1 Introduction; 9.2 The Limits of Distributed Associative Memories,and Beyond; 9.3 Properties of the Perceptron; 9.4 What Comes Next; Chapter 10:The Rosenblatt Program; 10.1 Introduction; 10.2 Installing the Program; 10.3 Training a Perceptron; 10.4 Testing What the Memory Has Learned; Chapter 11:Perceptrons and Logic Gates; 11.1 Introduction; 11.2 Boolean Algebra 327 $a11.3 Perceptrons and Two-Valued AlgebraChapter 12:Performing More Logic With Perceptrons; 12.1 Two-Valued Algebra and Pattern Spaces; 12.2 Perceptrons and Linear Separability; Appendix - The DawsonJots Font; Chapter 13:Value Units and Linear Nonseparability; 13.1 Linear Separability and Its Implications; 13.2 Value Units and the Exclusive-Or Relation; 13.3 Value Units and Connectedness; Chapter 14:Network By Problem Type Interactions; 14.1 All Networks Were Not Created Equally; 14.2 Value Units and the Two-Valued Algebra; Chapter 15:Perceptrons and Generalization; 15.1 Background 327 $a15.2 Generalization and Savings for the 9-Majority ProblemChapter 16:Animal Learning Theory and Perceptrons; 16.1 Discrimination Learning; 16.2 Linearly Separable Versions of Patterning; Chapter 17:The Multilayer Perceptron; 17.1 Creating Sequences of Logical Operations; 17.2 Multilayer Perceptrons and the Credit Assignment Problem; 17.3 The Implications of the Generalized Delta Rule; Chapter 18:The Rumelhart Program; 18.1 Introduction; 18.2 Installing the Program; 18.3 Training a Multilayer Perceptron; 18.4 Testing What the Network Has Learned; Chapter 19:Beyond the Perceptron 's Limits 327 $a19.1 Introduction 330 $aConnectionism is a "hands on" introduction to connectionist modeling through practical exercises in different types of connectionist architectures. explores three different types of connectionist architectures - distributed associative memory, perceptron, and multilayer perceptron provides a brief overview of each architecture, a detailed introduction on how to use a program to explore this network, and a series of practical exercises that are designed to highlight the advantages, and disadvantages, of each accompanied by a website at http://www.bcp.psych.ualbert 606 $aConnectionism 615 0$aConnectionism. 676 $a153 700 $aDawson$b Michael Robert William$f1959-$0900153 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020031603321 996 $aConnectionism$94421536 997 $aUNINA