I am going to classify multispectral remote sensing image using SVM .

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I have written a code for classifying three crops using SVM training , The three crops are cotton ,wheat and gram. Now to get more accuracy I want to optimize the training data using genetic algorithm then I wish to train the optimized data using SVM train and then want the classification result . First want to compare the result with simple SVM algorithm , I am attaching the codes here please help me to correct that code so that i could get the better kappa coefficient .ore if u suggest some other parameters of comparison of two techniques . One more thing is please suggest me how to use kernels in SVMtrain instruction ..!

Accepted Answer

Shashank Prasanna
Shashank Prasanna on 11 Apr 2014
The following section in the documentation shows how you can use cross validation to tune your svm parameters using optimization:
Go through the examples and how-to's in this link, that should answer most of your questions:
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Priyanka Athawale
Priyanka Athawale on 12 Apr 2014
Thank u sir , will u please explain me how can I use this in my program ?? and can validation help me to calculate accuracy of the classifier ?

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