Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension
Machine Learning - Special issue on computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Proofs for the optimality of classification in real-world machine learning situations are constructed. The validity of each proof requires reasoning about the probability of certain subsets of feature vectors. It is shown that linear discriminants classify by making the least demanding assumptions on the values of these probabilities. This enables measuring the confidence of classification by linear discriminants. We demonstrate experimentally that when linear discriminants make decisions with high confidence, their performance on real-world data improves significantly, to the point where they beat the best known nonlinear techniques on large portions of the data.