Learning from Positive Data and Negative Counterexamples: A Survey

The article presents state of the art on learning languages in the limit from full positive data and negative counterexamples to overextending conjectures. In the main model, the learner can store in its long-term memory all data seen so far. Variants of this model are considered where the learner always gets least counterexamples, or counterexamples bounded by the maximal size of positive data seen. All these variants are also considered for the model, where the learner does not have long-term memory, but can use the last conjecture. Capabilities, properties, and relationships between these models (and some other variations) are surveyed. Also, a variant of the main model restricted to learning classes definable by finite automata by learners definable by finite automata is considered.

[1]  S. Pinker.  Formal models of language learning , 1979, Cognition.

[2]  Sanjay Jain,et al.  Iterative Learning from Positive Data and Negative Counterexamples , 2007, ALT.

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

[4]  John Case,et al.  Incremental Concept Learning for Bounded Data Mining , 1997, Inf. Comput..

[5]  Steffen Lange,et al.  Algorithmic Learning for Knowledge-Based Systems: Gosler Final Report , 1995 .

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

[7]  R. Treiman,et al.  Brown & Hanlon revisited: mothers' sensitivity to ungrammatical forms , 1984, Journal of Child Language.

[8]  Thomas Zeugmann,et al.  Incremental Learning from Positive Data , 1996, J. Comput. Syst. Sci..

[9]  Wojciech Rytter,et al.  On the Maximal Number of Cubic Runs in a String , 2010, LATA.

[10]  Kenneth Wexler,et al.  Formal Principles of Language Acquisition , 1980 .

[11]  Sanjay Jain,et al.  Learnability of automatic classes , 2012, J. Comput. Syst. Sci..

[12]  Robin Milner,et al.  On Observing Nondeterminism and Concurrency , 1980, ICALP.

[13]  Sanjay Jain,et al.  Learning languages from positive data and negative counterexamples , 2008, J. Comput. Syst. Sci..

[14]  John Case,et al.  Comparison of Identification Criteria for Machine Inductive Inference , 1983, Theor. Comput. Sci..

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

[16]  Sanjay Jain,et al.  Iterative learning from texts and counterexamples using additional information , 2011, Machine Learning.

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

[18]  J. Hayes.  Cognition and the development of language , 1970 .

[19]  Thomas Zeugmann,et al.  A Guided Tour Across the Boundaries of Learning Recursive Languages , 1995, GOSLER Final Report.

[20]  Chen Lin,et al.  Neural Information Processing -letters and Reviews Simplify Support Vector Machines by Iterative Learning , 2022 .

[21]  D. Angluin.  Queries and Concept Learning , 1988 .

[22]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[23]  篠原 武.  Studies on inductive inference from positive data , 1986 .

[24]  Manuel Blum,et al.  A Machine-Independent Theory of the Complexity of Recursive Functions , 1967, JACM.

[25]  Sanjay Jain,et al.  Learning languages from positive data and a limited number of short counterexamples , 2007, Theor. Comput. Sci..

[26]  Sanjay Jain,et al.  Automatic Learning from Positive Data and Negative Counterexamples , 2012, ALT.

[27]  Sanjay Jain,et al.  On Learning Languages from Positive Data and a Limited Number of Short Counterexamples , 2006, COLT.

[28]  C. Snow,et al.  Feedback to first language learners: the role of repetitions and clarification questions , 1986, Journal of Child Language.

[29]  Jr. Hartley Rogers.  Theory of Recursive Functions and Effective Computability , 1969 .

[30]  John Case,et al.  Language Learning with Some Negative Information , 1993, J. Comput. Syst. Sci..