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% OSU Support Vector Machines (SVMs) Toolbox
% version 3.00, Feb. 2002
%
% The core of this toolbox is based on Dr. Lin's Lib SVM version 2.33
% For more details, please see:
% http://www.csie.ntu.edu.tw/~cjlin/libsvm
%
% Data Preprocessing:
%
% Normalize: normalize all the samples to make their energy is 1
% Scale: scale all the samples to a range, such as [-1 1]
%
% SVM classifier Trainer:
%
% LinearSVC (u_LinearSVC)
% - construct a linear C-SVM (nu-SVM) classifier
% from training samples.
% PolySVC (u_PolySVC)
% - construct a non-linear C-SVM (nu-SVM) classifier
% with a polynomial kernel.
% RbfSVC (u_RbfSVC)
% - construct a non-linear C-SVM (nu-SVM) classifier
% with a radial based kernel, or Gaussian kernel.
% one-RbfSVC
% - construct a non-linear 1-SVM with a radial based
% kernel, or Gaussian kernel.
%
% C-SVC Tester:
%
% SVMTest - test the performance of a trained
% SVM classifier
%%
% C-SVM Classifier:
%
% SVMClass - classify a set of input patterns
% given a trained SVM classifier
%
% Low level functions:
% (following functions are called by functions listed above)
% SVMClass,
% mexSVMTrain, mexSVMClass
%
% Plot results:
% SVMPlot2: plot out the training samples and classification boundaries of a two-class problem
% SVMPlot: plot out the training samples and classification boundaries of a multi-class problem,
% However, generally speaking, the plots obtained by this function is not very attractive.
%
% Demonstration functions:
% Demo\osusvmdemo - the main function of the command-line demonstration
%
% Demo\c_lindemo (u_lindemo)
% - demonstration for constructing and test a linear C-SVM, or nu-SVM,
% classifier
% Demo\c_poldemo (u_poldemo)
% - demonstration for constructing and test a nonlinear C-SVM, or nu-SVM,
% classifier with a polynomial kernel
% Demo\c_rbfdemo (u_rbfdemo)
% - demonstration for constructing and test a nonlinear C-SVM, or nu-SVM,
% classifier with a RBF kernel
% Demo\one_rbfdemo
% - demonstration for constructing and test a nonlinear 1-SVM
% with a RBF kernel
% Demo\c_clademo (u_clademo)
% - demonstration for classify a group of input patterns using
% the constructed SVM classifier.
% Demo\DemoData_train, Demo\DemoData_test, and Demo\DemoData_class - data used
% in this demonstration. they are HRR radar signatures
% generated from MSTAR data.
%----------------------------------------
% Authors:
% Junshui Ma (junshui@lanl.gov), NIS-2, Los Alamos National Lab
% Yi Zhao (zhaoy@ee.eng.ohio-state.edu), EE department, Ohio State University
%
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