Adaptivity and Learning [E-Book] : An Interdisciplinary Debate / edited by Reimer Kühn, Randolf Menzel, Wolfram Menzel, Ulrich Ratsch, Michael M. Richter, Ion-Olimpiu Stamatescu.
Kühn, Reimer, (editor)
Menzel, Randolf, (editor) / Menzel, Wolfram, (editor) / Ratsch, Ulrich, (editor) / Richter, Michael M., (editor) / Stamatescu, Ion-Olimpiu, (editor)
Berlin, Heidelberg : Springer, 2003
XII, 403 p. online resource.
Full Text
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.