Machine Learning and Perception

Name: Machine Learning and Perception
Brand: WORLD SCIENTIFIC PUB CO INC
Price: 193.75 AUD
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By: Guido Tascini (Editor), Primo Zingaretti (Editor), Floriana Esposito (Editor), Vito Roberto (Editor)

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Hardcover | 1 December 1995

At a Glance

Hardcover
216 Pages

Dimensions(cm)
22.86 x 16.51 x 1.27

Hardcover

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As perception stands for the acquisition of a real world representation by interaction with an environment, learning is the modification of this internal representation. This book highlights the relation between perception and learning and describes the influence of the learning in the interaction with the environment. Besides, this volume contains a series of applications of both machine learning and perception, where the former is often embedded in the latter and vice-versa. Among the topics covered, there are visual perception for autonomous robots, model generation of visual patterns, attentional reasoning, genetic approaches and various categories of neural networks.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Artificial Intelligence Machine Learning Neural Networks & Fuzzy Systems Booktopia Publisher Services World Scientific Publishing

Preface
Motivations in Artificial Organisms	p. 3
A Learning Approach to Neural Control	p. 20
Visual Perception and Conceptual Spaces	p. 39
What is there? Perception of Indoor Images	p. 47
A Perception System for Mobile Robot Localisation	p. 57
Visual Guidance for Autonomous Robots: A Case Study	p. 65
Intelligent Visual Sensing System for Autonomous Applications	p. 75
Depth Estimation by Adaptive Regulation of Camera Parameters	p. 83
A Neural Network for Optic Flow Computation through Subgraph Isomorphism	p. 91
On Visual Text Planning, Processing and Programming	p. 99
Synthesis of Shapes by Dynamic Data Fusion	p. 107
Attentional Reasoning	p. 115
Perceptually Motivated Model Generation for Template Matching	p. 127
From Machine Perception to Machine Learning: Generating Models of Visual Objects	p. 139
Boundary Detection using Genetic Algorithms	p. 149
Inductive Inference of Regular Grammars from Noisy Examples with Recurrent Neural Networks	p. 157
Knowledge Transplantation between Neural Networks	p. 165
Psycpgenetic Model for Behavioural Sequence Learning: Algorithmic Elements and Computational Results	p. 173
Towards the Learnability of Prolog Programs	p. 180
Adaptive Heuristic Search for Untractable Problems	p. 188
Local Associative Networks	p. 196
Author List	p. 205
Table of Contents provided by Blackwell. All Rights Reserved.

Machine Learning and Perception

At a Glance

Hardcover

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