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How to input train data and test data (features of images) using SVM calssifier

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This is the code that i have got for classification using SVM. Can any one tell me how should i input train data and test data in the code,...
% Classfication using SVM classifier.............
% 1. Load the sample data
load dataname
% 2. Create data, a two-column matrix containing sepal length and sepal width % measurements for 150 irises.
data = [meas(:,1), meas(:,2)];
% 3. From the species vector, create a new column vector, groups, to classify data % into two groups: data and non-data.
groups = ismember(dataset,'data');
% 4. Randomly select training and test sets.
[train, test] = crossvalind('holdOut',groups); cp = classperf(groups);
% 5. Train an SVM classifier using a linear kernel function and plot the grouped data.
svmStruct = svmtrain(data(train,:),groups(train),'showplot',true);
% 6. Add a title to the plot, using the KernelFunction field from the svmStruct % structure as the title.
title(sprintf('Kernel Function: %s',... func2str(svmStruct.KernelFunction)),... 'interpreter','none');
% 7. Use the svmclassify function to classify the test set.
classes = svmclassify(svmStruct,data(test,:),'showplot',true);
% 8. Evaluate the performance of the classifier.
classperf(cp,classes,test); cp.CorrectRate
% ans =
% 0.9867
% 9. Use a one-norm, hard margin support vector machine classifier by changing the
% boxconstraint property.
figure svmStruct = svmtrain(data(train,:),groups(train),... 'showplot',true,'boxconstraint',1e6);
classes = svmclassify(svmStruct,data(test,:),'showplot',true);
% 10. Evaluate the performance of the classifier.
classperf(cp,classes,test); cp.CorrectRate
%ans =
% 0.9867
  2 comentarios
sabiya fatima
sabiya fatima el 29 de Jul. de 2020
i am getting error in line 4:
Undefined function 'crossvalind' for input
arguments of type 'char'.
please help to solve it

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Respuestas (1)

Haddouche Abdelouahab
Haddouche Abdelouahab el 21 de Mzo. de 2017
I would like to help with my project represented in classification EEG using lda pca ica and svm
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