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Learning with Nested Generalized Exemplars

Name: Learning with Nested Generalized Exemplars
Brand: Springer, Berlin
SKU: 9780792391104
Price: 2567 CZK
Availability: InStock

Autor: Steven L. Salzberg

Jazyk:

Vazba: Pevná

Vydavatel: Springer, Berlin

Dostupnost: Skladem u dodavatele v malém množství

Odesíláme za 13-18 dnů

2 567 Kč

Machine Learning is one of the oldest and most intriguing areas of Ar tificial Intelligence. From th...

Informace o knize

Autor

Steven L. Salzberg

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

1990

Stránek

160

EAN

9780792391104

ISBN

0792391101

Enbook ID

01398033

Vydavatel

Springer, Berlin

Hmotnost

970

Rozměry

156 x 234 x 12

Kompletní popis

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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