Anbieter: Kloof Booksellers & Scientia Verlag, Amsterdam, Niederlande
Zustand: as new. Cambridge, MA: The MIT Press, 1994. Paperback. 500 pp.- These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems. In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve? English text. Condition : as new. Condition : as new copy. ISBN 9780262581264. Keywords : ,
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In den WarenkorbPaperback. Zustand: Brand New. 157 pages. 9.00x6.00x0.50 inches. In Stock.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 354 pages. 9.26x6.11x0.81 inches. In Stock.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: Brand New. reprint edition. 411 pages. 9.26x6.11x0.94 inches. In Stock.