Adaptivity and Learning: An Interdisciplinary Debate / Edition 1

by Reimer Kühn, Randolf Menzel, Wolfram Menzel, Ulrich Ratsch, Michael M. Richter

Overview

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.

Product Details

ISBN-13:	9783540000914
Publisher:	Springer Berlin Heidelberg
Publication date:	08/05/2003
Edition description:	2003
Pages:	403
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

Adaptivity and Learning — an Interdisciplinary Debate.- I Biology and Behaviour of Adaptation and Learning.- Biology of Adaptation and Learning.- The Adaptive Properties of the Phosphate Uptake System of Cyanobacteria: Information Storage About Environmental Phosphate Supply.- Cognitive Architecture of a Mini-Brain.- Cerebral Mechanisms of Learning Revealed by Functional Neuroimaging in Humans.- Creating Presence by Bridging Between the Past and the Future: the Role of Learning and Memory for the Organization of Life.- II Physics Approach to Learning — Neural Networks and Statistics.- The Physics Approach to Learning in Neural Networks.- Statistical Physics of Learning and Generalization.- The Statistical Physics of Learning: Phase Transitions and Dynamical Symmetry Breaking.- The Complexity of Learning with Supportvector Machines — A Statistical Physics Study.- III Mathematical Models of Learning.- Mathematics Approach to Learning.- Learning and the Art of Fault-Tolerant Guesswork.- Perspectives on Learning Symbolic Data with Connectionistic Systems.- Statistical Learning and Kernel Methods.- Inductive Versus Approximative Learning.- IV Learning by Experience.- Learning by Experience.- Learning by Experience from Others — Social Learning and Imitation in Animals and Robots.- Reinforcement Learning: a Brief Overview.- A Simple Model for Learning from Unspecific Reinforcement.- V Human-Like Cognition and AI Learning.- Aspects of Human-Like Cognition and AI Learning.- Making Robots Learn to See.- Using Machine Learning Techniques in Complex Multi-Agent Domains.- Learning Similarities for Informally Defined Objects.- Semiotic Cognitive Information Processing: Learning to Understand Discourse. A Systemic Model of Meaning Constitution.