does anybody have expertise with matlab SVM classifier?

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Tim 2013-2-22

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回答： Vinay Kumar 2015-2-9

Does anybody know if it's possible to extract more information from SVM classify than just the group? I'm hoping I'll be able to determine the distance from the class boundary. It can serve as a measure of confidence in the classification, or a fuzziness to the membership definition. It seems like that should be possible, I'm wondering if matlab has already implemented that functionality. If not, can anybody suggest a way to implement this as an accessory function to matlab's svm classification?

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Ilya 2013-2-22

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See my comments at the bottom of this thread http://www.mathworks.com/matlabcentral/answers/62757-how-do-i-find-slack-variables-in-svm-distance-to-the-boundary

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Tim 2013-2-22

在 MATLAB Online 中打开

That's really helpful, I just want to make sure I understand. Below is my code for predicting classes on a test dataset:

    Group = svmclassify(SVMStruct,Sample); %Sample is the test features
    sv = SVMStruct.SupportVectors;
    alphaHat = SVMStruct.Alpha;
    bias = SVMStruct.Bias;
    kfun = SVMStruct.KernelFunction;
    kfunargs = SVMStruct.KernelFunctionArgs;
    f = kfun(sv,Sample,kfunargs{:})'*alphaHat(:) + bias;

I ran it on a test set of 25, and saved Group in the first column and your f value in the second:

1 61.9034496803315

1 55.6097471320111

1 29.9168641220881

1 37.4841493478117

1 20.3962105716209

-1 79.6733478962262

1 45.3679636234752

1 49.0841383006749

1 72.6740709982137

1 42.9570874407634

1 56.3203630497531

-1 91.3203476908088

1 56.5131087391274

1 49.4198820528178

-1 97.4609560433329

-1 85.8169118173245

-1 94.1952199328395

-1 99.9702926453379

-1 97.1881289724361

-1 104.362322625414

-1 86.4060043918572

1 58.0004228062444

-1 88.3996251852743

-1 87.6174077335166

-1 87.6789516196668

I can see that all f values are positive. My understanding is the greater the f value, the further away it is from the boundary? Ie, an f value of 0.1 would mean a weak prediction?

Tim 2013-2-26

在 MATLAB Online 中打开

I didn't clean anything up, this is my complete function (just removed the header to made it easier to paste in). I'm a bit confused about lines 2 and 3 that you suggested for me. I thought I shouldn't be overwriting the changes made in lines 2 in line 3, so I changed that and it didn't fix the problem of f values not agreeing with predicted class.

 SampleScaleShift = bsxfun(@plus,Sample,SVMStruct.ScaleData.shift);
    Sample = bsxfun(@rdivide,SampleScaleShift,SVMStruct.ScaleData.scaleFactor);

I ran it on a different dataset and got much smaller values of f, but the sign on those f values still doesn't make sense to me. I'm sure the predicted class and the f value match up.

1 -94.2522619242825

-1 159.828900542196

1 63.7326650768443

-------------- complete function -----------------------------------

    Group = svmclassify(SVMStruct,Sample);
    Sample = bsxfun(@plus,Sample,SVMStruct.ScaleData.shift);
    Sample = bsxfun(@rdivide,Sample,SVMStruct.ScaleData.scaleFactor);
    sv = SVMStruct.SupportVectors;
    alphaHat = SVMStruct.Alpha;
    bias = SVMStruct.Bias;
    kfun = SVMStruct.KernelFunction;
    kfunargs = SVMStruct.KernelFunctionArgs;
    f = kfun(sv,Sample,kfunargs{:})'*alphaHat(:) + bias;
    Confidence = f;

Ilya 2013-2-26

Yes.

Matthijs 2013-9-12

I don't understand, how do you get a scalar value from Confidence? My sample is a 1x3000 or so vector and Confidence gives me a vector with 3000 or so values. If this is normal, how to obtain a scalar score for the confidence?

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