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About this product
- DescriptionThis book is based on the author's Ph.D. dissertation. 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 t 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 t 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.
- Author(s)Paul E. Utgoff
- PublisherSpringer-Verlag New York Inc.
- Date of Publication05/04/2012
- SubjectComputing: Professional & Programming
- Series TitleThe Springer International Series in Engineering and Computer Science
- Series Part/Volume Number15
- Place of PublicationNew York
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Notebiography
- Weight296 g
- Width155 mm
- Height235 mm
- Spine10 mm
- Edition StatementSoftcover reprint of the original 1st ed. 1986
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