(Enter summary)
Abstract: One view of computational learning theory is that of a learner acquiring the knowledge of
a teacher. We introduce a formal model of learning capturing the idea that teachers may have
gaps in their knowledge. In particular, we consider learning from a teacher who labels examples
"+" (a positive instance of the concept being learned), "\Gamma" (a negative instance of the concept
being learned), and "?" (an instance with unknown classification), in such a way that knowledge
of the concept... (Update)
Context of citations to this paper: More
.... learning with restricted focus of attention [3, 4] learning probabilistic concepts [9] learning with a consistently ignorant teacher [6] or learning with a constrained instance or projective equivalence oracle [2] In the sequel, we assume that the reader is familiar with...
...adversary instead of a random process, and the rate of incorrect answers that can be tolerated is consequently much lower. Frazier et al. [14] have introduced a model of omissions in answers to membership queries, called learning from a consistently ignorant teacher. The basic...
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BibTeX entry: (Update)
Mike Frazier, Sally Goldman, Nina Mishra, and Leonard Pitt. Learning from a consistently ignorant teacher. In Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, July 1994. http://citeseer.ist.psu.edu/frazier94learning.html More
@article{ frazier96learning,
author = "Michael Frazier and Sally A. Goldman and Nina Mishra and Leonard Pitt",
title = "Learning from a Consistently Ignorant Teacher",
journal = "Journal of Computer and System Sciences",
volume = "52",
number = "3",
pages = "471-492",
year = "1996",
url = "citeseer.ist.psu.edu/frazier94learning.html" }
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Introduction to Automata Theory (context) - Hopcroft, Ullman - 1979
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A theory of the learnable (context) - Valiant - 1984
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Learnability and the Vapnik-Chervonenkis dimension (context) - Blumer, Ehrenfeucht et al. - 1989
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Queries and concept learning (context) - Angluin - 1988
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Cryptographic limitations on learning Boolean formulae and f..
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Learning from noisy examples (context) - Angluin, Laird - 1988
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Efficient distribution-free learning of probabilistic concep..
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Efficient noise-tolerant learning from statistical queries
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Learning in the presence of malicious errors
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Learning conjunctions of Horn clauses
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Negative results for equivalence queries (context) - Angluin - 1990
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functions on randomly drawn points (context) - Haussler, Littlestone et al. - 1994
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Learning the CLASSIC description logic: Theoretical and expe..
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CLASSIC learning
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42
term DNF formulas with queries (context) - Blum, Rudich et al. - 1992
40
Types of noise in data for concept learning (context) - Sloan - 1988
36
Efficient learning with virtual threshold gates
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Exact learning Boolean functions via the monotone theory
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term DNF formulas using queries and counterexamples (context) - Angluin - 1987
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21
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17
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12
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Noise-tolerant parallel learning of geometric concepts
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6
Exact learning of -DNF formulas with malicious membership qu.. (context) - Angluin - 1994
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Learning DNF under the uniform distribution (context) - Jackson - 1994
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Special issue for the 23rd Annual ACM Symposium on Theory of.. (context) - Angluin, Kharitonov et al. - 1995
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