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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?
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George A. Drastal is Senior Research Scientist at Siemens Corporate Research.
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Book Description The MIT Press. PAPERBACK. Condition: New. 0262581264 New Condition. Seller Inventory # NEW99.1025420
Book Description A Bradford Book, 1994. Condition: New. book. Seller Inventory # M0262581264
Book Description Condition: New. New. Seller Inventory # STRM-0262581264