Adaptivity and Learning
Springer Berlin (Verlag)
978-3-642-05510-2 (ISBN)
To foster interdisciplinary dialogue, this book presents diverse perspectives from various scientific fields, including:
- The biological perspective: e.g., physiology, behaviour;
- The mathematical perspective: e.g., algorithmic and stochastic learning;
- The physics perspective: e.g., learning for artificial neural networks;
- The "learning by experience" perspective: reinforcement learning, social learning, artificial life;
- The cognitive perspective: e.g., deductive/inductive procedures, learning and language learning as a high level cognitive process;
- The application perspective: e.g., robotics, control, knowledge engineering.
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.
| Erscheint lt. Verlag | 16.11.2010 |
|---|---|
| Zusatzinfo | XII, 403 p. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 702 g |
| Themenwelt | Naturwissenschaften ► Physik / Astronomie ► Allgemeines / Lexika |
| Schlagworte | Adaptation • Adaptivity • Artificial Intelligence • Cognition • Complexity • Information Processing • Issue • learning • Learning Techniques • machine learning • Mathematica • Mathematics • Networks • neural network • Neural networks • robot • Statistics |
| ISBN-10 | 3-642-05510-9 / 3642055109 |
| ISBN-13 | 978-3-642-05510-2 / 9783642055102 |
| Zustand | Neuware |
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