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Abstract: Boosting algorithms for classification are based on altering the initial distribution assumed to underly a given example set. The idea of knowledge-based sampling (KBS) is to sample out prior knowledge and previously discovered patterns to achieve that subsequently applied data mining algorithms automatically focus on novel patterns without any need to adjust the base algorithm. This sampling strategy anticipates a user's expectation based on a set of constraints how to adjust the... (Update)
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BibTeX entry: (Update)
@misc{ scholz-comparing,
author = "Boosting Martin Scholz",
title = "Comparing Knowledge-Based Sampling to",
url = "citeseer.ist.psu.edu/765833.html" }
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