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    Machine Learning of Inductive Bias

Machine Learning of Inductive Bias

Paul E. Utgoff
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      This book is based on the author's Ph.D. dissertation[56]. The the­ sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre­ pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor­ mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob­ servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir­ able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.
      Format: Paperback / softback CONTRIBUTORS: Paul E. Utgoff EAN: 9781461294085 COUNTRY: United States PAGES: WEIGHT: 296 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag New York Inc. DATE PUBLISHED: 2012-04-05 CITY: GENRE: COMPUTERS / Artificial Intelligence / General WIDTH: 155 cm SPINE:

      Book Themes:

      Artificial intelligence

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      This book is based on the author's Ph.D. dissertation[56]. The the­ sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre­ pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor­ mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob­ servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir­ able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.
      Format: Paperback / softback CONTRIBUTORS: Paul E. Utgoff EAN: 9781461294085 COUNTRY: United States PAGES: WEIGHT: 296 g HEIGHT: 235 cm
      PUBLISHED BY: Springer-Verlag New York Inc. DATE PUBLISHED: 2012-04-05 CITY: GENRE: COMPUTERS / Artificial Intelligence / General WIDTH: 155 cm SPINE:

      Book Themes:

      Artificial intelligence

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      Be the first to write a review
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