How to deploy SVM on ARM Cortex-M processor

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Micael Coutinho
Micael Coutinho 2019-1-1
评论： Walter Roberson 2019-3-18
Hi everyone.
I have a project in which I have to deploy a SVM (support vector machine) model into an ARM Cortex-M processor. I have already successfully trained my SVM, but I don't know how to deploy it on my edge device (microcontroller). I know that there is a library for neural network (CMSIS NN), but it has little support, as far as I can see. Can anyone help?

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Walter Roberson
Walter Roberson 2019-1-1
You do code generation on a https://www.mathworks.com/help/stats/classificationsvm.html ClassificationSVM object using https://www.mathworks.com/help/stats/classreg.learning.classif.compactclassificationsvm.predict.html predict().
In your interactive MATLAB session, you save() the classification model you trained. In the code for use on the deployed machine, you load() the model and predict() using it.
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Nikhilesh Karanam
Nikhilesh Karanam 2019-3-15
Dear Walter Roberson,
Which interactive MATLAB session you mean? Could you please share the link of it? Thanks in advance :)
Regards,
Nikhilesh K

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Micael Coutinho
Micael Coutinho 2019-1-2
Thank you. It worked.
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Nikhilesh Karanam
Nikhilesh Karanam 2019-3-18
编辑：Nikhilesh Karanam 2019-3-18
Thanks. Well, yes. deploying the training portion is not possible. I have used classification learner App, selected Linear SVM for my project, trained the model got a validation accuracy of 98%. I generated a matlab script from the App and used the function for prediction of new data in the generated script which looks like this:
yfit = trainedClassifier.predictFcn(T2)
I get good results on MATLAB and I am stuck here. Please let me know how I can move forward from this point in generating C code if you have any idea. Thanks :)
Walter Roberson
Walter Roberson 2019-3-18
I am not sure. You might have to alter that to use
yfit = predict(trainedClassifier, T2);

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