Computational Learning Theory by Anthony (24 results)

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Soft cover. Condition: Very Good. All of our books without an ISBN number (normally pre-1970 in date) are described individually in detail. Books with an ISBN number (this one included) are all offered for sale in good condition or better: some may be in very good, near fine, or fine condition. If the condition is critical to your decision to purchase, then please contact us and we will let you know our view of its condition. If the book is very heavy, we will may need to contact you before completion of purchase to advise you of extra postage costs.

paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

New! Hard bound in rust cloth with bright silver titles, first edition, xi + Pp239. A new, unread copy without a dust jacket. 560 grams. A volume from The Institute of Mathematics & Its Applications Conference Series; New Series, Number 53.

Hardcover. Condition: Good.

Hardcover. Condition: Very Good. No Jacket. First Edition. VG condition. Lower front corners are a bit worn and there is a narrow three inch mark on the rear cover, otherwise the covers look great. The binding is tight. Interior pages are clean and unmarked. Small scuff on front flyleaf. USPS electronic tracking number issued free of charge.

Softcover. Condition: Très bon. Ancien livre de bibliothèque. Salissures sur la tranche. Edition 1997. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Stains on the edge. Edition 1997. Ammareal gives back up to 15% of this item's net price to charity organizations.

Paperback or Softback. Condition: New. Computational Learning Theory 0.8. Book.

Condition: New.

Condition: New.

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 171 | Sprache: Englisch | Produktart: Bücher.

Paperback. Condition: new. Paperback. Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics. Computational learning theory is one of the first attempts to construct a mathematical theory of a cognitive process. It has been a field of much interest and rapid growth in recent years. This text provides a framework for studying a variety of algorithmic processes, such as those currently in use for training artificial neural networks. The authors concentrate on an approximate model for learning and gradually develop the ideas of efficiency considerations. Finally, they consider applications of the theory to artificial neural networks. An abundance of exercises and an extensive list of references round out the text. This volume provides a comprehensive review of the topic, including information drawn from logic, probability, and complexity theory. It forms a solid introduction to the theory of comptutational learning suitable for a broad spectrum of graduate students from theoretical computer science to mathematics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New. pp. 172 67:B&W 6.69 x 9.61 in or 244 x 170 mm (Pinched Crown) Perfect Bound on White w/Gloss Lam.

Condition: New. pp. 172.

Condition: New. In.

Paperback. Condition: New.

Condition: New. pp. 172.

Paperback. Condition: Brand New. 157 pages. 10.00x7.00x0.50 inches. In Stock.

Paperback. Condition: new. Paperback. Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics. Computational learning theory is one of the first attempts to construct a mathematical theory of a cognitive process. It has been a field of much interest and rapid growth in recent years. This text provides a framework for studying a variety of algorithmic processes, such as those currently in use for training artificial neural networks. The authors concentrate on an approximate model for learning and gradually develop the ideas of efficiency considerations. Finally, they consider applications of the theory to artificial neural networks. An abundance of exercises and an extensive list of references round out the text. This volume provides a comprehensive review of the topic, including information drawn from logic, probability, and complexity theory. It forms a solid introduction to the theory of comptutational learning suitable for a broad spectrum of graduate students from theoretical computer science to mathematics. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This an introduction to the theory of computational learning.

Paperback. Condition: new. Paperback. Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics. Computational learning theory is one of the first attempts to construct a mathematical theory of a cognitive process. It has been a field of much interest and rapid growth in recent years. This text provides a framework for studying a variety of algorithmic processes, such as those currently in use for training artificial neural networks. The authors concentrate on an approximate model for learning and gradually develop the ideas of efficiency considerations. Finally, they consider applications of the theory to artificial neural networks. An abundance of exercises and an extensive list of references round out the text. This volume provides a comprehensive review of the topic, including information drawn from logic, probability, and complexity theory. It forms a solid introduction to the theory of comptutational learning suitable for a broad spectrum of graduate students from theoretical computer science to mathematics. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Condition: Brand New. 157 pages. 10.00x7.00x0.50 inches. In Stock. This item is printed on demand.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 400.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This is a self contained volume in which the authors concentrate on the probably approximately correct model . It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretic.