Connectionism: A Hands-on Approach

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John Wiley & Sons, 15.4.2008 - 208 sivua
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Connectionism is a “hands on” introduction toconnectionist modeling through practical exercises in differenttypes of connectionist architectures.
  • explores three different types of connectionist architectures– distributed associative memory, perceptron, and multilayerperceptron
  • provides a brief overview of each architecture, a detailedintroduction on how to use a program to explore this network, and aseries of practical exercises that are designed to highlight theadvantages, and disadvantages, of each
  • accompanied by a website athttp://www.bcp.psych.ualberta.ca/~mike/Book3/ that includespractice exercises and software, as well as the files and blankexercise sheets required for performing the exercises
  • designed to be used as a stand-alone volume or alongsideMinds and Machines: Connectionism and Psychological Modeling(by Michael R.W. Dawson, Blackwell 2004)
 

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Sisältö

Chapter 1HandsOn Connectionism
1
Chapter 2The Distributed Associative Memory
5
Chapter 3The James Program
9
Chapter 4Introducing Hebb Learning
22
Chapter 5Limitations of Hebb Learning
30
Chapter 6Introducing the Delta Rule
37
Chapter 7Distributed Networks and Human Memory
41
Chapter 8Limitations of Delta Rule Learning
46
Chapter 17The Multilayer Perceptron
108
Chapter 18The Rumelhart Program
114
Chapter 19Beyond the Perceptron s Limits
129
Chapter 20Symmetry as a Second Case Study
133
Chapter 21How Many Hidden Units?
137
Chapter 22Scaling Up With the Parity Problem
145
Chapter 23Selectionism and Parity
151
Chapter 24Interpreting a Small Network
157

Chapter 9The Perceptron
48
Chapter 10The Rosenblatt Program
58
Chapter 11Perceptrons and Logic Gates
72
Chapter 12Performing More Logic With Perceptrons
81
Chapter 13Value Units and Linear Nonseparability
86
Chapter 14Network By Problem Type Interactions
91
Chapter 15Perceptrons and Generalization
94
Chapter 16Animal Learning Theory and Perceptrons
99
Chapter 25Interpreting Networks of Value Units
163
Chapter 26Interpreting Distributed Representations
174
Chapter 27Creating Your Own Training Sets
183
References
188
Index of Names
195
Subject Index
198
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Michael R. W. Dawson is a member of the Department of Psychology and the Biological Computation Project at the University of Alberta, Canada. He is the author of Understanding Cognitive Science (Blackwell , 1998) and Minds and Machines (Blackwell, 2004).

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