title: Excel2SVM: a stand-alone Python tool for data analysis via support vector machines.
abstract: The creation of classification kernel models to categorize unknown data samples of massive magnitude is an extremely advantageous tool for the scientific community. Excel2SVM, a stand-alone Python mathematical analysis tool, bridges the gap between researchers and computer science to create a simple graphical user interface that allows users to examine data and perform maximal margin classification. This valuable ability to train support vector machines and classify unknown data files is harnessed in this fast and efficient software, granting researchers full access to this complicated, high-level algorithm. Excel2SVM offers the ability to convert data to the proper sparse format while performing a variety of kernel functions along with cost factors/modes, grids, crossvalidation, and several other functions. This program functions with any type of quantitative data making Excel2SVM the ideal tool for analyzing a wide variety of input. The software is free and available at www.bioinformatics.org/excel2svm. A link to the software may also be found at www.kernel-machines.org. This software provides a useful graphical user interface that has proven to provide kernel models with accurate results and data classification through a decision boundary.
authors.affiliation: Walter Reed Army Institute of Research, Molecular Pathology, Silver Spring, Maryland 20910, USA. Matthew.Hellman@duke.edu
authors.forename: Matthew
authors.initials: M
authors.lastname: Hellman
authors.forename: Marti
authors.initials: M
authors.lastname: Jett
authors.forename: Rasha
authors.initials: R
authors.lastname: Hammamieh
mesh_terms: D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D014584:User-Computer Interface