Adaptivity and Learning

An Interdisciplinary Debate

By: Reimer KÃ¼hn (Editor), Randolf Menzel (Editor), Wolfram Menzel (Editor)

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Hardcover | 20 May 2003

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420 Pages

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23.5 x 15.88 x 1.91

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Adaptivity and learning have in recent decades become a common concern of scientific disciplines. These issues have arisen in mathematics, physics, biology, informatics, economics, and other fields more or less simultaneously. The aim of this publication is the interdisciplinary discourse on the phenomenon of learning and adaptivity. Different perspectives are presented and compared to find fruitful concepts for the disciplines involved. The authors select problems showing representative traits concerning the frame up, the methods and the achievements rather than to present extended overviews.

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Adaptivity and Learning - an Interdisciplinary Debate	p. 1
Biology of Adaptation and Learning	p. 7
The Adaptive Properties of the Phosphate Uptake System of Cyanobacteria: Information Storage About Environmental Phosphate Supply	p. 11
Cognitive Architecture of a Mini-Brain	p. 23
Cerebral Mechanisms of Learning Revealed by Functional Neuroimaging in Humans	p. 49
Creating Presence by Bridging Between the Past and the Future: the Role of Learning and Memory for the Organization of Life	p. 59
The Physics Approach to Learning in Neural Networks	p. 73
Statistical Physics of Learning and Generalization	p. 77
The Statistical Physics of Learning: Phase Transitions and Dynamical Symmetry Breaking	p. 89
The Complexity of Learning with Supportvector Machines - A Statistical Physics Study	p. 101
Mathematics Approach to Learning	p. 111
Learning and the Art of Fault-Tolerant Guesswork	p. 115
Perspectives on Learning Symbolic Data with Connectionistic Systems	p. 141
Statistical Learning and Kernel Methods	p. 161
Inductive Versus Approximative Learning	p. 187
Learning by Experience	p. 213
Learning by Experience from Others - Social Learning and Imitation in Animals and Robots	p. 217
Reinforcement Learning: a Brief Overview	p. 243
A Simple Model for Learning from Unspecific Reinforcement	p. 265
Aspects of Human-Like Cognition and AI Learning	p. 283
Making Robots Learn to See	p. 285
Using Machine Learning Techniques in Complex Multi-Agent Domains	p. 311
Learning Similarities for Informally Defined Objects	p. 329
Semiotic Cognitive Information Processing: Learning to Understand Discourse. A Systemic Model of Meaning Constitution	p. 347
Table of Contents provided by Blackwell. All Rights Reserved.

Adaptivity and Learning

An Interdisciplinary Debate

At a Glance

Hardcover

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How to return your order

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