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    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Machine Learning of Inductive Bias

Machine Learning of Inductive Bias

Utgoff, Paul E.

Hardcover

Series: The Springer International Engineering and Computer Science, Book 15

General ComputersGeneral Gardening

ISBN10: 0898382238
ISBN13: 9780898382235
Publisher: Springer Nature
Published: Jun 30 1986
Pages: 166
Weight: 1.01
Height: 0.87 Width: 6.54 Depth: 9.30
Language: English
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.

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