Articles liés à Learning With Nested Generalized Exemplars
Synopsis
Machine Learning is one of the oldest and most intriguing areas of Ar- tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa- tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com- puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in- clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di- agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica- tion areas.
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Learning with nested generalized exemplars Steven L. Salzberg. Communications and information theory 100; The international series in engineering and computer science 100.
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Boston, Kluwer [1990]., 1990
ISBN 10 : 0792391101
ISBN 13 : 9780792391104
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Learning with Nested Generalized Exemplars (The Springer International Series in Engineering and Computer Science) Salzberg, Steven L.
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Springer, 1990
ISBN 10 : 0792391101
ISBN 13 : 9780792391104
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Hardcover. Etat : Used: Good. former library 1990 hc no dj withdrawn stamp in book/ on edge of pages clean text 159 pages/// L-14. N° de réf. du vendeur 0809M8YSV0P
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Learning with Nested Generalized Exemplars (The Springer International Series in Engineering and Computer Science)
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Springer, 1990
ISBN 10 : 0792391101
ISBN 13 : 9780792391104
Ancien ou d'occasion
Couverture rigide
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Learning with Nested Generalized Exemplars (The Springer International Series in Engineering and Computer Science, 100)
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Learning with Nested Generalized Exemplars (The Springer International Series in Engineering and Computer Science, 100)
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Learning with Nested Generalized Exemplars
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Learning with Nested Generalized Exemplars
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Learning with Nested Generalized Exemplars (The Springer International Series in Engineering and Computer Science, 100)
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Learning with Nested Generalized Exemplars
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Springer Us Mai 1990, 1990
ISBN 10 : 0792391101
ISBN 13 : 9780792391104
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Buch. Etat : Neu. Neuware - Machine Learning is one of the oldest and most intriguing areas of Ar tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conjures up. Nevertheless, a solid foundation of theory and technique has begun to develop around a variety of specialized learning tasks. Such tasks in clude discovery of optimal or effective parameter settings for controlling processes, automatic acquisition or refinement of rules for controlling behavior in rule-driven systems, and automatic classification and di agnosis of items on the basis of their features. Contributions include algorithms for optimal parameter estimation, feedback and adaptation algorithms, strategies for credit/blame assignment, techniques for rule and category acquisition, theoretical results dealing with learnability of various classes by formal automata, and empirical investigations of the abilities of many different learning algorithms in a diversity of applica tion areas. N° de réf. du vendeur 9780792391104
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Learning with Nested Generalized Exemplars (The Springer International Series in Engineering and Computer Science)
Edité par
Springer, 1990
ISBN 10 : 0792391101
ISBN 13 : 9780792391104
Ancien ou d'occasion
Couverture rigide
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