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Neurocomputing

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Cover of 'Neurocomputing'

Table of Contents

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    Book Overview
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    Chapter 1 Incorporating knowledge in multi-layer networks: the example of protein secondary structure prediction
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    Chapter 2 Product Units with Trainable Exponents and Multi-Layer Networks
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    Chapter 3 Recurrent Backpropagation and Hopfield Networks
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    Chapter 4 Optimization of the number of hidden cells in a multilayer perceptron. Validation in the linear case
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    Chapter 5 Single-layer learning revisited: a stepwise procedure for building and training a neural network
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    Chapter 6 Synchronous Boltzmann Machines and Gibbs Fields: Learning Algorithms
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    Chapter 7 Fast Computation of Kohonen Self-Organization
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    Chapter 8 Learning algorithms in neural networks: recent results
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    Chapter 9 Statistical approach to the Jutten-Hérault algorithm
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    Chapter 10 The N Programming Language
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    Chapter 11 Neural Networks Dynamics
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    Chapter 12 Dynamical Analysis of Classifier Systems
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    Chapter 13 Neuro-Computing Aspects in Motor Planning & Control
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    Chapter 14 Neural Networks and Symbolic A.I
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    Chapter 15 Integrated artificial neural networks: components for higher level architectures with new properties
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    Chapter 16 Basic VLSI Circuits for Neural Networks
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    Chapter 17 An Analog VLSI Architecture for Large Neural Networks
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    Chapter 18 Analog implementation of a permanent unsupervised learning algorithm
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    Chapter 19 An Analog Cell for VLSI Implementation of Neural Networks
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    Chapter 20 Use of Pulse Rate and Width Modulations in a Mixed Analog/digital Cell for Artificial Neural Systems
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    Chapter 21 Parallel Implementation of a Multi-Layer Perceptron
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    Chapter 22 A monolithic processor array for stochastic relaxation using optical random number generation
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    Chapter 23 Dedicated neural network A retina for edge detection
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    Chapter 24 Neural Network Applications in the Edinburgh Concurrent Supercomputer Project
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    Chapter 25 The Semi-Parallel Architectures of Neuro-Computers
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    Chapter 26 Speech coding with multilayer networks
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    Chapter 27 Statistical Inference in Multilayer Perceptrons and Hidden Markov Models with Applications in Continuous Speech Recognition
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    Chapter 28 Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition
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    Chapter 29 Data Compression Using Multilayer Perceptrons
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    Chapter 30 Guided Propagation: current state of theory and applications
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    Chapter 31 Speaker adaptation using multi-layer feed-forward automata and canonical correlation analysis
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    Chapter 32 Analysis of linear predictive data as speech and of ARMA processes by a class of single-layer connectionist models
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    Chapter 33 High Level Speech Processing by Competitive Neural Networks From Psychology to Simulation
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    Chapter 34 Connected Word Recognition Using Neural Networks
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    Chapter 35 Handwritten Digit Recognition: Applications of Neural Net Chips and Automatic Learning
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    Chapter 36 A method to de-alias the scatterometer wind field: a real world application
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    Chapter 37 Detection of microcalcifications in mammographic images
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    Chapter 38 What is a feature, that it may define a character, and a character, that it may be defined by a feature ?
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    Chapter 39 A Study of Image Compression with Backpropagation
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    Chapter 40 Distortion Invariant Image Recognition by Madaline and Back-Propagation Learning Multi-Networks
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    Chapter 41 An Algorithm for Optical Flow
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    Chapter 42 Multicellular Processing Units for Neural Networks: Model of Columns in the Cerebral Cortex
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    Chapter 43 A Potentially Powerful Connectionist Unit: The Cortical Column
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    Chapter 44 Complex information processing in real neurones
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    Chapter 45 Formal Approach and Neural Network Simulation of the Co-ordination between Posture and Movement
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    Chapter 46 Cheapmonkey: Comparing an ANN and the Primate Brain on a Simple Perceptual Task: Orientation Discrimination
Attention for Chapter 4: Optimization of the number of hidden cells in a multilayer perceptron. Validation in the linear case
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Chapter title
Optimization of the number of hidden cells in a multilayer perceptron. Validation in the linear case
Chapter number 4
Book title
Neurocomputing
Published by
Springer, Berlin, Heidelberg, January 1990
DOI 10.1007/978-3-642-76153-9_4
Book ISBNs
978-3-64-276155-3, 978-3-64-276153-9
Authors

François Vallet, Vallet, François