Learning Nested Generalized Exemplars (22 results)

Condition: Used: Good. former library 1990 hc no dj withdrawn stamp in book/ on edge of pages clean text 159 pages/// L-14.

Condition: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. C 563 9780792391104 Sprache: Englisch Gewicht in Gramm: 550.

Hardcover. Condition: New. US SELLER SHIPS FAST FROM USA.

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Condition: New.

Condition: New. pp. 184.

Condition: New. pp. 184.

Taschenbuch. Condition: Neu. Learning with Nested Generalized Exemplars | Steven L. Salzberg | Taschenbuch | xx | Englisch | 2011 | Springer | EAN 9781461288305 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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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Hardcover. Condition: Like New. Like New. book.

Paperback. Condition: Like New. Like New. book.

Buch. Condition: 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.

Condition: new. Questo è un articolo print on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 184 pp. Englisch.

Condition: New. Print on Demand pp. 184 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Condition: New. Print on Demand pp. 184 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 184.

Condition: New. PRINT ON DEMAND pp. 184.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 184 pp. Englisch.