Computational learning theory: survey and selected bibliography
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
Characterizations of learnability for classes of {O, …, n}-valued functions
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
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
Hybrid pattern recognition system capable of self-modification
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Fat-shattering and the learnability of real-valued functions
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning Separations by Boolean Combinations of Half-Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extending Elementary Formal Systems
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming
WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures
Advanced elementary formal systems
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
The effectiveness of corpus-induced dependency grammars for post-processing speech
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
On data classification by iterative linear partitioning
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Some connections between learning and optimization
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
A formalization of explanation-based macro-operator learning
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
On data classification by iterative linear partitioning
Discrete Applied Mathematics
Some connections between learning and optimization
Discrete Applied Mathematics
Probably approximately correct learning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Universal ε-approximators for integrals
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Learning-Related complexity of linear ranking functions
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Data reduction for weighted and outlier-resistant clustering
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Learning with stochastic inputs and adversarial outputs
Journal of Computer and System Sciences
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This paper presents some results on the probabilistic analysis of learning, illustrating the applicability of these results to settings such as connectionist networks. In particular, it concerns the learning of sets and functions from examples and background information. After a formal statement of the problem, some theorems are provided identifying the conditions necessary and sufficient for efficient learning, with respect to measures of information complexity and computational complexity. Intuitive interpretations of the definitions and theorems are provided.