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[null,"1.","Record Nr.",null,"UNINA9910830778203321"]
[null,null,"Autore",null,"Dawson Michael R. W"]
[null,null,"Titolo",null,"Connectionism [[electronic resource] ] : A Hands-on Approach"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, : Wiley, 2008"]
[null,null,"ISBN",null,"1-4051-4387-8"]
[{"$nulls":4},"1-280-19855-9"]
[{"$nulls":4},"9786610198559"]
[{"$nulls":4},"0-470-69407-6"]
[{"$nulls":4},"1-4051-4389-4"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (210 p.)"]
[null,null,"Disciplina",null,"153"]
[null,null,"Soggetti",null,"Connectionism"]
[{"$nulls":4},"Social Sciences"]
[{"$nulls":4},"Psychology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,"Nota di contenuto",null,"CONNECTIONISM; 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"]
[{"$nulls":4},"4.3 Hebb Learning of Orthonormal,Non-Basis VectorsAppendix - Creating mutually orthogonal vectors with Maple; Chapter 5:Limitations of Hebb Learning; 5.1 Introduction; 5.2 The Effect of Repetition; 5.3 The Effect of Correlation; Appendix - Creating the linearly independent set of vectors; Chapter 6:Introducing the Delta Rule; 6.1 Introduction; 6.2 The Delta Rule; 6.3 The Delta Rule and the Effect of Repetition; 6.4 The Delta Rule and the Effect of Correlation; Chapter 7:Distributed Networks and Human Memory; 7.1 Background on the Paired Associate Paradigm"]
[{"$nulls":4},"7.2 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; "]
[{"$nulls":4},"9.1 Introduction; 9.2 The Limits of Distributed Associative Memories,and Beyond; 9.3 Properties of the Perceptron; 9.4 What Comes Next; Chapter 10:The Rosenblatt Program; 10.1 Introduction; 10.2 Installing the Program; 10.3 Training a Perceptron; 10.4 Testing What the Memory Has Learned; Chapter 11:Perceptrons and Logic Gates; 11.1 Introduction; 11.2 Boolean Algebra"]
[{"$nulls":4},"11.3 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"]
[{"$nulls":4},"15.2 Generalization and Savings for the 9-Majority ProblemChapter 16:Animal Learning Theory and Perceptrons; 16.1 Discrimination Learning; 16.2 Linearly Separable Versions of Patterning; Chapter 17:The Multilayer Perceptron; 17.1 Creating Sequences of Logical Operations; 17.2 Multilayer Perceptrons and the Credit Assignment Problem; 17.3 The Implications of the Generalized Delta Rule; Chapter 18:The Rumelhart Program; 18.1 Introduction; 18.2 Installing the Program; 18.3 Training a Multilayer Perceptron; 18.4 Testing What the Network Has Learned; Chapter 19:Beyond the Perceptron 's Limits"]
[{"$nulls":4},"19.1 Introduction"]
[null,null,null,"Sommario/riassunto","Connectionism 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"]