Articoli correlati a Learning with Nested Generalized Exemplars: 100

Learning with Nested Generalized Exemplars: 100 - Brossura

Salzberg, Steven L.

 
9781461288305: Learning with Nested Generalized Exemplars: 100
Brossura
ISBN 10: 1461288304 ISBN 13: 9781461288305
Casa editrice: Springer, 2011

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer
Data di pubblicazione
2011
Lingua
Inglese
ISBN 10 
1461288304
ISBN 13 
9781461288305
Rilegatura
Copertina flessibile
Numero di pagine
160
Contatto del produttore
Springer Nature Customer Service Center GmbH
ProductSafety@springernature.com

Europaplatz 3
Heidelberg
69115
Germania