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Published by Boston: Kluwer, 1990
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.
Book
Condition: Good. 356 pp., hardcover, ex library but text & binding clean & tight. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.
Published by Springer 1989-12-31, 1989
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: Hay-on-Wye Booksellers, Hay-on-Wye, HEREF, United Kingdom
Book
Hardcover. Condition: Very Good. Inscriptions to inner front cover and title page, light foxing to top edge. Contents vg - clean and unmarked, square and sound.
Published by Springer US, 1989
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: Buchpark, Trebbin, Germany
Book
Condition: Sehr gut. Zustand: Sehr gut - Gepflegter, sauberer Zustand. Außen: verschmutzt. Aus der Auflösung einer renommierten Bibliothek. Kann Stempel beinhalten. | Seiten: 372 | Sprache: Englisch.
Published by Springer, 2011
ISBN 10: 1461288177ISBN 13: 9781461288176
Seller: booksXpress, Bayonne, NJ, U.S.A.
Book
Soft Cover. Condition: new.
Published by Springer, 1989
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: booksXpress, Bayonne, NJ, U.S.A.
Book
Hardcover. Condition: new.
Published by Springer US, 2011
ISBN 10: 1461288177ISBN 13: 9781461288176
Seller: AHA-BUCH GmbH, Einbeck, Germany
Book
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses. The effectiveness of learning algorithms is very sensitive to this choice of language; choosing too large a language permits too many possible hypotheses for a program to consider, precluding effective learning, but choosing too small a language can prohibit a program from being able to find acceptable hypotheses. This dependence is not just a pitfall, however; it is also an opportunity. The work of Saul Amarel over the past two decades has demonstrated the effectiveness of representational shift as a problem-solving technique. An increasing number of machine learning researchers are building programs that learn to alter their language to improve their effectiveness. At the Fourth Machine Learning Workshop held in June, 1987, at the University of California at Irvine, it became clear that the both the machine learning community and the number of topics it addresses had grown so large that the representation issue could not be discussed in sufficient depth. A number of attendees were particularly interested in the related topics of constructive induction, problem reformulation, representation selection, and multiple levels of abstraction. Rob Holte, Larry Rendell, and I decided to hold a workshop in 1988 to discuss these topics. To keep this workshop small, we decided that participation be by invitation only.
Published by Springer, 2011
ISBN 10: 1461288177ISBN 13: 9781461288176
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Book
Condition: New.
Published by Springer, 1989
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Book
Condition: New.
Published by Springer US Sep 2011, 2011
ISBN 10: 1461288177ISBN 13: 9781461288176
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Book Print on Demand
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses. The effectiveness of learning algorithms is very sensitive to this choice of language; choosing too large a language permits too many possible hypotheses for a program to consider, precluding effective learning, but choosing too small a language can prohibit a program from being able to find acceptable hypotheses. This dependence is not just a pitfall, however; it is also an opportunity. The work of Saul Amarel over the past two decades has demonstrated the effectiveness of representational shift as a problem-solving technique. An increasing number of machine learning researchers are building programs that learn to alter their language to improve their effectiveness. At the Fourth Machine Learning Workshop held in June, 1987, at the University of California at Irvine, it became clear that the both the machine learning community and the number of topics it addresses had grown so large that the representation issue could not be discussed in sufficient depth. A number of attendees were particularly interested in the related topics of constructive induction, problem reformulation, representation selection, and multiple levels of abstraction. Rob Holte, Larry Rendell, and I decided to hold a workshop in 1988 to discuss these topics. To keep this workshop small, we decided that participation be by invitation only. 372 pp. Englisch.
Published by Springer US Dez 1989, 1989
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Book Print on Demand
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses. The effectiveness of learning algorithms is very sensitive to this choice of language; choosing too large a language permits too many possible hypotheses for a program to consider, precluding effective learning, but choosing too small a language can prohibit a program from being able to find acceptable hypotheses. This dependence is not just a pitfall, however; it is also an opportunity. The work of Saul Amarel over the past two decades has demonstrated the effectiveness of representational shift as a problem-solving technique. An increasing number of machine learning researchers are building programs that learn to alter their language to improve their effectiveness. At the Fourth Machine Learning Workshop held in June, 1987, at the University of California at Irvine, it became clear that the both the machine learning community and the number of topics it addresses had grown so large that the representation issue could not be discussed in sufficient depth. A number of attendees were particularly interested in the related topics of constructive induction, problem reformulation, representation selection, and multiple levels of abstraction. Rob Holte, Larry Rendell, and I decided to hold a workshop in 1988 to discuss these topics. To keep this workshop small, we decided that participation be by invitation only. 372 pp. Englisch.
Published by Springer US, 1989
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: moluna, Greven, Germany
Book Print on Demand
Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses. .
Published by Springer US, 2011
ISBN 10: 1461288177ISBN 13: 9781461288176
Seller: moluna, Greven, Germany
Book Print on Demand
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses. .
Published by Springer, 1989
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Book Print on Demand
Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Published by Springer, 2011
ISBN 10: 1461288177ISBN 13: 9781461288176
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Book Print on Demand
Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book.
Published by Springer US, 1989
ISBN 10: 0792390555ISBN 13: 9780792390558
Seller: AHA-BUCH GmbH, Einbeck, Germany
Book
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses. The effectiveness of learning algorithms is very sensitive to this choice of language; choosing too large a language permits too many possible hypotheses for a program to consider, precluding effective learning, but choosing too small a language can prohibit a program from being able to find acceptable hypotheses. This dependence is not just a pitfall, however; it is also an opportunity. The work of Saul Amarel over the past two decades has demonstrated the effectiveness of representational shift as a problem-solving technique. An increasing number of machine learning researchers are building programs that learn to alter their language to improve their effectiveness. At the Fourth Machine Learning Workshop held in June, 1987, at the University of California at Irvine, it became clear that the both the machine learning community and the number of topics it addresses had grown so large that the representation issue could not be discussed in sufficient depth. A number of attendees were particularly interested in the related topics of constructive induction, problem reformulation, representation selection, and multiple levels of abstraction. Rob Holte, Larry Rendell, and I decided to hold a workshop in 1988 to discuss these topics. To keep this workshop small, we decided that participation be by invitation only.
Published by SPRINGER NATURE, 2011
ISBN 10: 1461288177ISBN 13: 9781461288176
Seller: Russell Books, Victoria, BC, Canada
Book
Softcover. Condition: New. Special order direct from the distributor.