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Abstract: 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... (Update)
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BibTeX entry: (Update)
@misc{ bernick-pattern,
author = "Machines Jonathan Bernick",
title = "Pattern Classification with Polynomial Learning",
url = "citeseer.ist.psu.edu/759257.html" }
Citations (may not include all citations):196 Linear Algebra and Its Applications (context) - Strang - 1980
143 An Introduction to Support Vector Machines and Other Kernelb.. (context) - Cristianini, Shawe-Taylor - 2000
1 Pattern Classification Using Support Vector Machine Ensemble (context) - Kim, Pang et al. - 2002
1 Fast Algorithm of Support Vector Machines in Lung Cancer Dia.. (context) - Liu, Shen et al. - 2001
1 Fast SVM Training Algorithm with Decomposition on Very Large.. (context) - Dong, Krzyzak et al. - 2005
1 Benchmarking a Reduced Multivariate Polynomial Pattern Class.. (context) - Toh, Tran et al. - 2004
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
http://www.sgi.com/tech/mlc/db/
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