Characterizations of Class Preserving Monotonic and Dual Monotonic Language Learning

The present paper deals with monotonic and dual monotonic language learning from positive as well as from positive and negative examples. The three notions of monotonicity re ect di erent formalizations of the requirement that the learner has to always produce better and better generalizations when fed more and more data on the concept to be learnt. The three versions of dual monotonicity describe the concept that the inference device has to exclusively produce specializations that t better and better to the target language. We characterize strong{monotonic, monotonic, weak{monotonic, dual strong{monotonic, dual monotonic and monotonic & dual monotonic as well as nite language learning from positive data in terms of recursively generable nite sets.

[1]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[2]  Rolf Wiehagen Limes-Erkennung rekursiver Funktionen durch spezielle Strategien , 1975, J. Inf. Process. Cybern..

[3]  Manuel Blum,et al.  Toward a Mathematical Theory of Inductive Inference , 1975, Inf. Control..

[4]  Rolf Wiehagen,et al.  Charakteristische Eigenschaften von erkennbaren Klassen rekursiver Funktionen , 1976, J. Inf. Process. Cybern..

[5]  Dana Angluin,et al.  Inductive Inference of Formal Languages from Positive Data , 1980, Inf. Control..

[6]  Dana Angluin,et al.  Finding Patterns Common to a Set of Strings , 1980, J. Comput. Syst. Sci..

[7]  Takeshi Shinohara,et al.  Polynomial Time Inference of Extended Regular Pattern Languages , 1983, RIMS Symposium on Software Science and Engineering.

[8]  John Case,et al.  Machine Inductive Inference and Language Identification , 1982, ICALP.

[9]  Thomas Zeugmann,et al.  A-posteriori Characterizations in Inductive Inference of Recursive Functions , 1983, J. Inf. Process. Cybern..

[10]  Hermann A. Maurer,et al.  Theoretische Grundlagen der Programmiersprachen , 1984, Reihe Informatik.

[11]  Jerome A. Feldman,et al.  Learning automata from ordered examples , 1991, COLT '88.

[12]  John Case The power of vacillation , 1988, COLT '88.

[13]  Sanjay Jain,et al.  Recursion Theoretic Characterizations of Language Learning , 1989 .

[14]  Leonard Pitt,et al.  A polynomial-time algorithm for learning k-variable pattern languages from examples , 1989, COLT '89.

[15]  Daniel N. Osherson,et al.  Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists , 1990 .

[16]  Klaus P. Jantke,et al.  Monotonic and Nonmonotonic Inductive Inference of Functions and Patterns , 1990, Nonmonotonic and Inductive Logic.

[17]  Wen-Guey Tzeng,et al.  Learning String Patterns and Tree Patterns from Examples , 1990, ML.

[18]  Rolf Wiehagen A Thesis in Inductive Inference , 1990, Nonmonotonic and Inductive Logic.

[19]  Thomas Zeugmann,et al.  Types of monotonic language learning and their characterization , 1992, COLT '92.

[20]  Thomas Zeugmann,et al.  On the Power of Monotonic Language Learning , 1992 .

[21]  Shyam Kapur,et al.  Monotonic Language Learning , 1992, ALT.

[22]  Thomas Zeugmann,et al.  Learning Recursive Languages with Bounded Mind Changes , 1993, Int. J. Found. Comput. Sci..

[23]  Thomas Zeugmann,et al.  Characterization of language learning front informant under various monotonicity constraints , 1994, J. Exp. Theor. Artif. Intell..