Michiyo Yamaguchi  (email@example.com)
Shinichi Shimozono  (firstname.lastname@example.org)
Takeshi Shinohara  (email@example.com)
 Department of Artificial Intelligence
 Department of Control Engineering and Science
Kyushu Institute of Technology
Kawazu 680-4, Iizuka 820, Japan
We propose a learning algorithm that discovers a motif represented by patterns and an alphabet indexing from biosequences. From only positive examples with the help of an alphabet indexing, the algorithm finds k regular patterns as a k-minimal multiple generalization (k-mmg for short). The computational results for transmembrane domains indicate that the combination of k-mmg and alphabet indexing works quite successful. We also introduce a partial alphabet indexing that transforms symbols dependently on the position in sequences.