Communications of the ACM
A hard-core predicate for all one-way functions
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Learning decision trees using the Fourier spectrum
SIAM Journal on Computing
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Agnostically Learning Halfspaces
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Agnostically learning decision trees
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A Neyman-Pearson approach to statistical learning
IEEE Transactions on Information Theory
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It is well known that in many applications erroneous predictions of one type or another must be avoided. In some applications, like spam detection, false positive errors are serious problems. In other applications, like medical diagnosis, abstaining from making a prediction may be more desirable than making an incorrect prediction. In this paper we consider different types of reliable classifiers suited for such situations. We formalize the notion and study properties of reliable classifiers in the spirit of agnostic learning (Haussler, 1992; Kearns, Schapire, and Sellie, 1994), a PAC-like model where no assumption is made on the function being learned. We then give two algorithms for reliable agnostic learning under natural distributions. The first reliably learns DNFs with no false positives using membership queries. The second reliably learns halfspaces from random examples with no false positives or false negatives, but the classifier sometimes abstains from making predictions.