Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
STOC '84 Proceedings of the sixteenth annual ACM symposium on Theory of computing
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Learnable and Nonlearnable Visual Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
A computational model of teaching
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Journal of the ACM (JACM)
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Analysis of upper bound in Valiant's model for learning bounded CNF expressions
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Journal of the ACM (JACM)
Being taught can be faster than asking questions
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
DNF—if you can't learn'em, teach'em: an interactive model of teaching
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Learning with maximum-entropy distributions
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Exact learning of tree patterns from queries and counterexamples
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning with Maximum-Entropy Distributions
Machine Learning
Efficient distribution-free population learning of simple concepts
Annals of Mathematics and Artificial Intelligence
On-line learning with malicious noise and the closure algorithm
Annals of Mathematics and Artificial Intelligence
Learnability of Quantified Formulas
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
PAC Learning from Positive Statistical Queries
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Learnability of quantified formulas
Theoretical Computer Science
Representing knowledge in learning systems by pseudo boolean functions
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
Learning intersection-closed classes with signatures
Theoretical Computer Science
Learning one-counter languages in polynomial time
SFCS '87 Proceedings of the 28th Annual Symposium on Foundations of Computer Science
Computer Vision and Image Understanding
A formal framework for speedup learning from problems and solutions
Journal of Artificial Intelligence Research
A complete and tight average-case analysis of learning monomials
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Oblivious PAC learning of concept hierarchies
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Recursive teaching dimension, learning complexity, and maximum classes
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
The bacterial strains characterization problem
Proceedings of the 2011 ACM Symposium on Applied Computing
Minimum multiple characterization of biological data using partially defined boolean formulas
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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This paper deals with the learnability of Boolean functions. An intuitively appealing notion of dimensionality is developed and used to identify the most general class of Boolean function families that are learnable from polynomially many positive examples with one-sided error. It is then argued that although bounded DNF expressions lie outside this class, they must have efficient learning algorithms as they are well suited for expressing many human concepts. A framework that permits efficient learning of bounded DNF functions is identified.