Michael Kearns
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- Efficient noise-tolerant learning from statistical queries
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Efficient noise-tolerant learning from statistical queries
In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” learning algorithms in the most general way, we formalize ...
On the Efficiency of Noise-Tolerant PAC Algorithms Derived from Statistical Queries
The Statistical Query (SQ) model provides an elegant means for generating noise-tolerant PAC learning algorithms that run in time inverse polynomial in the noise rate. Whether or not there is an SQ algorithm for every noise-tolerant PAC algorithm that ...
PAC Learning from Positive Statistical Queries
ALT '98: Proceedings of the 9th International Conference on Algorithmic Learning TheoryLearning from positive examples occurs very frequently in natural learning. The PAC learning model of Valiant takes many features of natural learning into account, but in most cases it fails to describe such kind of learning. We show that in order to ...
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