The “Cross Wavelet and Wavelet Coherence Toolbox” download link by Grinsted et al (http://www.pol.ac.uk/home/research/waveletcoherence/) is dead. We will send you an active download link upon your request (an email with the download link will be sent to you automatically after you fill the form below):
To learn more about wavelet coherence analysis, see https://www.alivelearn.net/?s=wavelet
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Dear Prof. Xu,
I am writing to get more information about SVM classification.
I have one matrix with the inputs of the training data (X) and matrix with the labels (e.g. Y, my labels are -1, 1). I would like to do cross validation with n-fold (for example n=10) test on my data and I am going to obtain accuracy, Sensitivity and Specificity parameters to determine the performance of classifier.
I did my classification based on your tutorial (http://www.alivelearn.net/?s=libsvm).
bestcv = 0;
for log2c = -1:3,
for log2g = -4:1,
cmd = [‘-v 5 -c ‘, num2str(2^log2c), ‘ -g ‘, num2str(2^log2g)];
cv = svmtrain(heart_scale_label, heart_scale_inst, cmd);
if (cv >= bestcv),
bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;
end
fprintf(‘%g %g %g (best c=%g, g=%g, rate=%g)\n’, log2c, log2g, cv, bestc, bestg, bestcv);
end
end
Based on this code I can obtain cross validation accuracy, but in addition to accuracy I would like to obtain “predicted_label” according to cross validation.
Would you please guide me how can I obtain final predicted_label after cross validation.
Many thanks for kindly considering my question.
I look forward to hearing from you.
Best,
Iman
The new site for our cross wavelet and wavelet coherence toolbox is here:
http://www.glaciology.net/wavelet-coherence