A machine discovery from amino acid sequences by decision trees over regular patterns

DOI10.1007/BF03037183zbMath0780.68099OpenAlexW2038580939MaRDI QIDQ688817

Takeshi Shinohara, Ayumi Shinohara, Setsuo Arikawa, Yasuhito Mukouchi, Satoru Kuhara, Satoru Miyano

Publication date: 31 January 1994

Published in: New Generation Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf03037183

zbMATH Keywords

decision treePAC learningprotein structure predictionmachine discoverypattern language

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Decision lists over regular patterns., Distinguishing pattern languages with membership examples, The evolution of stochastic regular motifs for protein sequences, Gold-Style Learning Theory, Parallelism Increases Iterative Learning Power, Towards a mathematical theory of machine discovery from facts, Automatic learning from positive data and negative counterexamples, Inductive inference of unbounded unions of pattern languages from positive data, U-shaped, iterative, and iterative-with-counter learning, Parallelism increases iterative learning power

Cites Work

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