Support Vector Machines only classify data into two classes. This function removes that restriction by "searching" for the correct class for each row in the test data set.
This code is a clarification and optimization of Anand Mishra's code found here:
http://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine
Use only with more than 2 classes, otherwise use svmtrain() directly.
Usage Example:
%% SVM Multiclass Example
% SVM is inherently one vs one classification.
% This is an example of how to implement multiclassification using the
% one vs all approach.
TrainingSet=[ 1 10;2 20;3 30;4 40;5 50;6 66;3 30;4.1 42];
TestSet=[3 34; 1 14; 2.2 25; 6.2 63];
GroupTrain=[1;1;2;2;3;3;2;2];
results = multisvm(TrainingSet, GroupTrain, TestSet);
disp('multi class problem');
disp(results);
인용 양식
Cody (2024). Multi Class SVM (https://www.mathworks.com/matlabcentral/fileexchange/39352-multi-class-svm), MATLAB Central File Exchange. 검색됨 .
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