2002 Fiscal Year Final Research Report Summary
INDUCTIVE LEARNING OF DECISION TREES OVER REGULAR PATTERNS AND ITS APPLICATION TO GENOME INFORMATICS
| Project/Area Number |
13680457
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Section | 一般 |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | OSAKA PREFECTURE UNIVERSITY |
|---|
Principal Investigator |
SATO Masako Osaka Prefecture University, Department of Mathematics and Information Sciences, Professor, 総合科学部, 教授 (50081419)
|
|---|
| Co-Investigator(Kenkyū-buntansha) |
MUKOUCHI Yasuhito Osaka Prefecture University, Department of Mathematics and Information Sciences, Assistant Professor, 総合科学部, 助教授 (00264820)
|
|---|
| Project Period (FY) |
2001 – 2002
|
| Keywords | Inductive learning / Regular pattern / Decision tree / Positive example / Inductive inference / Compactness / Genome Informatics / Formal Language |
|---|
| Research Abstract |
Our main results are as follows:
1)We have investigated the problem of learning decision trees over regular patterns from a view point of knowledge discovery for Genome information processing. We first obtained some results about relations between the semantics containment and the syntactic containment of decision trees over regular patterns. Then we gave an efficient learning algorithm for some kind of decision trees with at most depth two over regular patterns from positive examples.
2) Compactness for a class of unions of regular patterns garantees an equivalency between semantic containment and syntactic containment, and plays an important role for designing efficient learning algorithm of unions of regular pattern languages. We obtained a neccesary and sufficient condition for a class of unions of regular patterns to have compactness.
3)We proposed refutable/inductive learning model from neighborhood examples, and apply it to the regular pattern languages.
4) Furthermore, we investigate the fundamental theory on regular pattern languages which plays an important role for designing efficient learning algorithms in the above problems.
|
