On the relationship between models for learning in helpful environments

Rajesh Parekh, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


The PAC and other equivalent learning models are widely accepted models for polynomial learnability of concept classes. However, negative results abound in the PAC learning framework (concept classes such as deterministic finite state automata (DFA) are not efficiently learnable in the PAC model). The PAC model’s requirement of learnability under all conceivable distributions could be considered too stringent a restriction for practical applications. Several models for learning in more helpful environments have been proposed in the literature including: learning from example based queries [2], online learning allowing a bounded number of mistakes [14], learning with the help of teaching sets [7], learning from characteristic sets [5], and learning from simple examples [12,4]. Several concept classes that are not learnable in the standard PAC model have been shown to be learnable in these models In this paper we identify the relationships between these different learning models. We also address the issue of unnatural collusion between the teacher and the learner that can potentially trivialize the task of learning in helpful environments.

Original languageEnglish (US)
Title of host publicationGrammatical Inference
Subtitle of host publicationAlgorithms and Applications - 5th International Colloquium, ICGI 2000, Proceedings
EditorsArlindo L. Oliveira
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783540452577
StatePublished - 2000
Event5th International Colloquium on Grammatical Inference, ICGI 2000 - Lisbon, Portugal
Duration: Sep 11 2000Sep 13 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th International Colloquium on Grammatical Inference, ICGI 2000

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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