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renorm2

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03-11-2016
12:26 AM

9 Views

linear svm decision function

3 Replies

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VictoriyaS_F_Intel

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03-14-2016
08:08 AM

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Hello,

Linear decision function in SVM model is defined as *D( x) = w* ∙ x + b. *And the hyperplane normal

*w*** = ∑y _{k} a_{k }x_{k},*

where *x** _{k }*and

You can read them using the methods svm::Model::getSupportVectors() and svm::Model::getClassificationCoefficients(), respectively.

For additional details please see chapter 2.1 in B. E. Boser, I. Guyon, and V. Vapnik. *A training algorithm for optimal margin classiﬁers.* Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp: 144–152.

Best regards,

Victoriya

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renorm2

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03-24-2016
02:18 AM

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Thanks for explaining.

There is one more question I would like to ask.

When using linear svm there is no need to add const bias term to every feature, i.e. bias term is taken care by the algorithm itself.

Is that right?

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VictoriyaS_F_Intel

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03-24-2016
08:07 AM

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Yes, there is no need to add bias term to each feature.

SVM training algorithm computes bias term as part of a model. svm::Model::getBias() method returns the value of bias term.

For more complete information about compiler optimizations, see our Optimization Notice.