ovrtrainBot.m

function [model] = ovrtrainBot(y, x, cmd)

% This function builds K svm models, each responsible for class k versus the

% rest.

% INPUT:

% x: an N x D data/feature matrix where N denotes the number of observations and

% D denotes feature dimensionality

% y: an N x 1 label vector, where each element y(n) in the nth row denotes

% the label of the observation x(n,:)

% cmd: the argument to control svm package

% Remarks:

% 1) The code uses all the observations given in x for training

% 2) The label y is multiclass label, i.e. # of class can be greater than 2

labelSet = unique(y);

% Note: the labels in y are not necessarily in the non-skippy order, for

% instance, the labels can be {-1 3 10}.

labelSetSize = length(labelSet);

models = cell(labelSetSize,1);

for i=1:labelSetSize

    models{i} = svmtrain(double(y == labelSet(i)), x, cmd);

end

model = struct('models', {models}, 'labelSet', labelSet);

% Note:

% 1) the model models{i} is corresponding to the label labelSet(i)

% 2) When wanting to access the ith train model, use model.models{i}

% 3) to access the label use model.labelSet, and for the ith label, use

% model.labelSet(i)

function [model] = ovrtrain(y, x, cmd)

labelSet = unique(y);

labelSetSize = length(labelSet);

models = cell(labelSetSize,1);

for i=1:labelSetSize

    models{i} = svmtrain(double(y == labelSet(i)), x, cmd);

end

model = struct('models', {models}, 'labelSet', labelSet);