Learning probabilistic classifiers under computational resource constraints
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چکیده
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.
منابع مشابه
Learning classifiers that perform probabilistic classification under computational resource constraints
Existing online machine learning techniques are often tuned to minimized computational resources, and are not able to utilize further resources when they are available. This is likely to result in sub-optimal learning performance in many online applications where the computational resources available will vary from time to time. This paper analyzes categories of computational resources and defi...
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تاریخ انتشار 2005