9780792390558 - Change of Representation and Inductive Bias: 87 (The Springer International Series in Engineering and Computer Science, 87) (12 resultados)

Condición: 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.

Hardcover. Condición: Bon. Ancien livre de bibliothèque. Edition 1989. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Edition 1989. Ammareal gives back up to 15% of this item's net price to charity organizations.

Condición: Very Good. Inscriptions to inner front cover and title page, light foxing to top edge. Contents vg - clean and unmarked, square and sound.

Condición: Sehr gut. Zustand: Sehr gut | Seiten: 356 | Sprache: Englisch | Produktart: Bücher | 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.

Condición: New. In.

Condición: New. Editor(s): Benjamin, Paul. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 356 pages, biography. BIC Classification: UYQ. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 22. Weight in Grams: 698. . 1989. Hardback. . . . .

Condición: New. pp. 372.

Gebunden. Condición: New. 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. .

Condición: New. Editor(s): Benjamin, Paul. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 356 pages, biography. BIC Classification: UYQ. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 22. Weight in Grams: 698. . 1989. Hardback. . . . . Books ship from the US and Ireland.

Buch. Condición: Neu. 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.

Condición: New. PRINT ON DEMAND pp. 372.

Condición: New. Print on Demand pp. 372 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.