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Learning probabilistic classifiers under computational resource constraints

In many online applications of machine learning, the computational resources available will vary from time-to-time. Surprisingly, existing techniques are designed to accommodate the minimum expected resources, and fail to utilize further resources when they are available. This paper presents an analysis of the relevant categories of computational resource involved, and presents an algorithm that starts with the classification time and accuracy of naive Bayes and can utilize increasing amounts of time to increase classification accuracy.