Pattern Classification with Polynomial Learning Machines
نویسنده
چکیده
We present a new kind of pattern classifier, the Polynomial Learning Machine (PLM). The PLM is derived, and its construction detailed. We compare the performance of the PLM to that of the Support Vector Machine (SVM) in classifying binary-classed data sets, and find that the PLM consistently trains and tests in a shorter time than the SVM while maintaining comparable classification accuracy; in the case of very large data sets, the PLM training and testing times are shorter by orders of magnitude. We conclude that the PLM is a useful tool for pattern classification, and worthy of further investigation.
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