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Classification using a radial basic function (RBF) SVM

I am using sklearn.svm.SVC (kernel = 'rbf') to classify the image data, which works very well. Linear SVM classifies data by placing a hyperplane between two classes. In case of rbf SVM, the plane will be in infinite dimension. For any test point, we can use a forecast to check where it belongs. In the linear case, we can manually get a prediction by obtaining the hyperplane equation. How can we do this in the case of SVB SVC. How accurately is performance predicted for SVB SVB.

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First things

Whenever we classify, we must consider:

  • Classifiers can be learned for large spaces, without actually matching points in arrogant space.
  • Data can be split linearly in high-dimensional space, but not linearly split in original feature space
  • Kernels can be used for SVM because of the dot product in double form, but can also be used elsewhere - they are not tied to the SVM formalism.
  • Kernels are also applied to objects that are not vectors enter image description here

For example, I'll put some of the kernels in use. enter image description here

For an SVM classifier with a Gaussian kernel, we'd have something like:

enter image description here Since you have noticed that the support vector has been replaced and therefore we can vary it depending on the results, for example, consider two functions and their colored dots: enter image description here

And by setting some values, we get: enter image description here

Now enter image description here



Or enter image description here

Now that infinity happens:

enter image description here

Then: enter image description hereenter image description here

What about adaBoost playing with datasets http://cseweb.ucsd.edu/~yfreund/adaboost/

If you like you can test the NETLAB ML Matlab software by Ian Nabney here

Here are more sources for SVMs

  • Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer (2006), ISBN 0-38-731073-8.
  • Hasti, Tibbirani, Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009. Pdf available online.
  • Jan Witten and Abe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, 2005.
  • David McKay, "Information Theory, Conclusions, and Learning Algorithms", which is freely available on the Internet!
  • Tom Mitchell, "Machine Science", McGraw Hill, 1997
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