TY - GEN
T1 - Typed pattern languages and their learnability
AU - Koshiba, Takeshi
PY - 1995/1/1
Y1 - 1995/1/1
N2 - In this paper, we extend patterns, introduced by Angluin [Ang80b], to typed patterns by introducing types into variables. A type is a recursive language and a variable of the type is substituted only with an element in the recursive language. This extension enhances the expressive power of patterns with preserving their good properties. First, we give a general learnability result for typed pattern languages. We show that if a class of types has finite elasticity then the typed pattern language is identifiable in the limit from positive data. Next, we give a useful tool to show the conservative learnability of typed pattern languages. That is, if an indexed family L of recursive languages has recursive finite thickness and the equivalence problem for L is decidable, then L is conservatively learnable from positive data. Using this tool, we consider the following classes of types: (1) the class of all strings over subsets of the alphabet, (2) the class of all untyped pattern languages, and (3) a class of ĸ-bounded regular languages. We show that each of these typed pattern languages is conservatively learnable from positive data.
AB - In this paper, we extend patterns, introduced by Angluin [Ang80b], to typed patterns by introducing types into variables. A type is a recursive language and a variable of the type is substituted only with an element in the recursive language. This extension enhances the expressive power of patterns with preserving their good properties. First, we give a general learnability result for typed pattern languages. We show that if a class of types has finite elasticity then the typed pattern language is identifiable in the limit from positive data. Next, we give a useful tool to show the conservative learnability of typed pattern languages. That is, if an indexed family L of recursive languages has recursive finite thickness and the equivalence problem for L is decidable, then L is conservatively learnable from positive data. Using this tool, we consider the following classes of types: (1) the class of all strings over subsets of the alphabet, (2) the class of all untyped pattern languages, and (3) a class of ĸ-bounded regular languages. We show that each of these typed pattern languages is conservatively learnable from positive data.
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U2 - 10.1007/3-540-59119-2_192
DO - 10.1007/3-540-59119-2_192
M3 - Conference contribution
AN - SCOPUS:84955606048
SN - 9783540591191
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 367
EP - 379
BT - Computational Learning Theory - 2nd European Conference, EuroCOLT 1995, Proceedings
A2 - Vitanyi, Paul
PB - Springer Verlag
T2 - 2nd European Conference on Computational Learning Theory, EuroCOLT 1995
Y2 - 13 March 1995 through 15 March 1995
ER -