Titlebook: Learning and Geometry: Computational Approaches; David W. Kueker,Carl H. Smith Book 1996 Birkhäuser Boston 1996 algebra.artificial intelli

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BRACE

Learning by MDLMachine learning has been formalized as the problem of estimating a conditional distribution as the ‘concept’ to be learned. The learning algorithm is based upon the MDL (Minimum Description Length) principle. The asymptotically optimal learning rate is determined for a typical example.

背心

Pac Learning, Noise, and GeometryThis paper describes the probably approximately correct model of concept learning, paying special attention to the case where instances are points in Euclidean n-space. The problem of learning from noisy training data is also studied.

ALIBI

Geometry Theorem Proving in Euclidean, Descartesian, Hilbertian and Computerwise FashionThe evolution and development of geometry theorem-proving, dating from Euclid’s “Elements” in 3c B.C., may be divided into several stages as indicated in the title of the paper. Achievements in recent years due to the Mathematics-Mechanization Group of the Institute of Systems Science, Academia Sinica are also briefly described.

Pander

让步

978-1-4612-8646-2Birkhäuser Boston 1996

OVERT

Learning and Geometry: Computational Approaches978-1-4612-4088-4Series ISSN 2297-0576 Series E-ISSN 2297-0584

grovel

A Review of Some Extensions to the PAC Learning Modelmalize the notion of learning from examples. In this paper, we review several extensions to the basic PAC model with a focus on the information complexity of learning. The extensions discussed are learning over a class of distributions, learning with queries, learning functions, and learning from generalized samples.

explicit

largesse

2297-0576 ns to artificial intelligence became apparent, the new field grew rapidly. The learning of geo­ metric objects became a natural area of study. The possibility of using learning techniques to compensate for unsolvability provided an attraction for individ­ uals with an immediate need to solve such di