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Slimsvm : A Simple Implementation Of Support Vector Machine For Analysis Of Microarray Data, Avik Karmaker
Slimsvm : A Simple Implementation Of Support Vector Machine For Analysis Of Microarray Data, Avik Karmaker
Theses
Support Vector Machine (SVM) is a supervised machine learning technique being widely used in multiple areas of biological analysis including microarray data analysis. SlimSVM has been developed with the intention of replacing OSU SVM as the classification component of GenoIterSVM in order to make it independent of other SVM packages. GenolterSVM, developed by Dr. Marc Ma, is a SVM implementation with an iterative refinement algorithm for improved accuracy of classification of genotype microarray data. SlimSVM is an object-oriented, modular, and easy-to-use implementation written in C++. It supports dot (linear) and polynomial (non-linear) kernels. The program has been tested with artificial …