Probabilistic inductive inference: A survey

Andris Ambainis

doi:10.1016/S0304-3975(00)00218-8

Probabilistic inductive inference: A survey

Andris Ambainis^*

^*Corresponding author for this work

University of California at Berkeley

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

The developments in probabilistic inductive inference, learning, of recursive functions are presented. The paradigm of finite learning is analyzed. Classes of functions identifiable by probabilistic algorithms with different probabilities of correct answer are investigated.

Original language	English
Pages (from-to)	155-167
Number of pages	13
Journal	Theoretical Computer Science
Volume	264
Issue number	1
DOIs	https://doi.org/10.1016/S0304-3975(00)00218-8
Publication status	Published - 2001
Externally published	Yes

Keywords

Computational learning theory
Inductive inference
Randomized computation

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10.1016/S0304-3975(00)00218-8

Cite this

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KW - Randomized computation

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DO - 10.1016/S0304-3975(00)00218-8

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EP - 167

JO - Theoretical Computer Science

JF - Theoretical Computer Science

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Probabilistic inductive inference: A survey

Abstract

Keywords

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