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One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques- tion that is central to understanding how computers might learn: "how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept?" Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis- sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi- mally consistent hypotheses, even in the presence of certain types of incon- sistencies in the data. More generally, it provides a framework for integrat- ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration.
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Incremental Version-Space Merging: A General Framework for Concept Learning (Studies in Risk and Uncertainty) , foreword by Tom M. Mitchell ,
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Kluwer Academic Publishers, 1990
ISBN 10 : 0792391195
ISBN 13 : 9780792391197
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Hardcover / Pappeinband , Etat : Gut. 115 Seiten / Pages , berieben , 11-6 ISBN 0792391195 Sprache: Englisch Gewicht in Gramm: 408. N° de réf. du vendeur 143279
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Incremental Version-Space Merging: A General Framework for Concept Learning
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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. T. N° de réf. du vendeur 5971276
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Incremental Version-Space Merging: A General Framework for Concept Learning (The Springer International Series in Engineering and Computer Science, 104)
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Incremental Version-Space Merging: A General Framework for Concept Learning
Edité par
Springer US, Springer US Jul 1990, 1990
ISBN 10 : 0792391195
ISBN 13 : 9780792391197
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques tion that is central to understanding how computers might learn: 'how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept ' Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi mally consistent hypotheses, even in the presence of certain types of incon sistencies in the data. More generally, it provides a framework for integrat ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 136 pp. Englisch. N° de réf. du vendeur 9780792391197
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Incremental Version-Space Merging: A General Framework for Concept Learning
Edité par
Springer US, Springer New York, 1990
ISBN 10 : 0792391195
ISBN 13 : 9780792391197
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques tion that is central to understanding how computers might learn: 'how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept ' Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi mally consistent hypotheses, even in the presence of certain types of incon sistencies in the data. More generally, it provides a framework for integrat ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration. N° de réf. du vendeur 9780792391197
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Incremental Version-Space Merging: A General Framework for Concept Learning
impression à la demandeVendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
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Hardback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 401. N° de réf. du vendeur C9780792391197
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Incremental Version-Space Merging: A General Framework for Concept Learning
Edité par
Springer US Jul 1990, 1990
ISBN 10 : 0792391195
ISBN 13 : 9780792391197
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -One of the most enjoyable experiences in science is hearing a simple but novel idea which instantly rings true, and whose consequences then begin to unfold in unforeseen directions. For me, this book presents such an idea and several of its ramifications. This book is concerned with machine learning. It focuses on a ques tion that is central to understanding how computers might learn: 'how can a computer acquire the definition of some general concept by abstracting from specific training instances of the concept ' Although this question of how to automatically generalize from examples has been considered by many researchers over several decades, it remains only partly answered. The approach developed in this book, based on Haym Hirsh's Ph.D. dis sertation, leads to an algorithm which efficiently and exhaustively searches a space of hypotheses (possible generalizations of the data) to find all maxi mally consistent hypotheses, even in the presence of certain types of incon sistencies in the data. More generally, it provides a framework for integrat ing different types of constraints (e.g., training examples, prior knowledge) which allow the learner to reduce the set of hypotheses under consideration. 136 pp. Englisch. N° de réf. du vendeur 9780792391197
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Incremental Version-Space Merging
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Incremental Version-Space Merging
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Etat : New. Print on Demand pp. 136 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam. N° de réf. du vendeur 5845290
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Incremental Version-Space Merging
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Etat : New. PRINT ON DEMAND pp. 136. N° de réf. du vendeur 183051263
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