| Project/Area Number |
09680372
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
SHINOHARA Takeshi Kyushu Institute of Technology, Department of Artificial Intelligence, Professor, 情報工学部, 教授 (60154225)
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| Co-Investigator(Kenkyū-buntansha) |
SUGIMOTO Noriko Kyushu Institute of Technology, Department of Artificial Intelligence, Assistant, 情報工学部, 教務職員 (80271120)
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| Project Period (FY) |
1997 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥2,900,000 (Direct Cost: ¥2,900,000)
Fiscal Year 1999: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1998: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1997: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Inductive Inference / Machine Learning / Inductive Inference from Positive Data / Pattern Languages / Elementary Formal Systems / Efficiency of Learning |
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| Research Abstract |
The aim of this research is in investigating realizability of machine learning, by studying inductive inference as a theoretical model of learning from examples. In general, examples using in learning are categorized in positive ones and negative ones. In language (or grammar) learning, positive examples are corresponding to (grammatically) correct sentences. Data obtained from experiments can be considered as positive examples of a certain property, when they are concerned with the property. In this research, we have considered theoretical limits of inductive learning based on positive examples and investigated efficient learning algorithms from the viewpoint of practical applications.
A pattern is a string consisting of constant symbols and variables. The language or a pattern is the set of constant strings obtained by substituting nonempty constant strings for variables in the pattern. For any fixed k, the class of unions of at most k pattern languages is already shown to be inferable from positive data.
We apply a learning algorithm for pattern languages to discover a motif from amino-acid sequences. From only positive examples with the help of an alphabet indexing, the algorithm successfully finds sets or patterns, that can be considered as motifs.
Elementary formal systems are logic programs over patterns, and therefore natural extension of patterns. We employ elementary formal systems as a unifying framework for language learning. Within this framework, we have shown various results, such as, model inference style language learning, and the existence or rich classes of languages inferable from positive data.
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