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Abstract: This paper introduces a new machine learning technique, Weighted Probability Distribution Voting (WPDV). During learning, WPDV determines the output class probability distribution for each input feature, both atomic and complex. During classification, WPDV takes all input features that occur in the new input and adds the corresponding probability distributions, each multiplied by a weight factor which depends on the feature or feature type. The output class with the highest sum is then... (Update)
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BibTeX entry: (Update)
@misc{ halteren-weighted,
author = "Hans van Halteren",
title = "Weighted Probability Distribution Voting, an introduction",
url = "citeseer.ist.psu.edu/432149.html" }
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