Pre-Processing Technique Using Support Vector Machines Has Diagnostic Applications

A novel pre-processing technique has been developed for use with support vector machines (SVMs).

Called kernel alignment, the method has been adopted by a number of scientists in the field for use in feature selection as well as kernel optimization. Kernel optimization is important for ensuring accuracy of classifications performed by SVMs and other learning machines. Examples of uses of kernel alignment include gene expression analysis for medical diagnostics and biomarker identification, protein homology detection, identification of quantitative structure activity relationships (QSARs) for potential drug leads, and image reconstruction.

Health Discovery Corporation (HDC; Savannah, GA, USA) announced that the U.S. Patent and Trademark Office (USPTO) issued a notice of allowance on its patent application for the novel pre-processing technique for use with support vector machines (SVMs).

Once the USPTO issues the patent described above, Health Discovery Corporation will hold the exclusive rights to 23 issued U.S. and foreign patents covering uses of SVMs for discovery of knowledge from large data sets. The issued patents cover methods and systems for pre-processing of data to enhance knowledge discovery using SVMs, analysis of data using multiple support vector machines and for multiple data sets, and providing SVM analysis services over the Internet.

Stephen D. Barnhill, M.D., HDC's chairman and CEO, commented, "We remain committed to monetizing our technology through expansion of our patent portfolio, collaboration with our licensing partners, and protection of our intellectual property rights when necessary.”


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