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Add support for SVDD (one-class SVM) #2807
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Seems nice. I'll take it. |
Is work underway on this or are there any plans on releasing this? I require it for some work and will attempt to implement it myself (using LibSVM) if needs be. |
Would love to see such an implementation native to scikit-learn as well |
I want to know how to optimize the parameters (v and s), we have no test set, how to evaluate model is best ? |
zhaoqidong: open research question ;) @MichaelAquilina : sorry for the long turn-around. There are currently no plans. I'm not sure if patching the current libsvm we have in scikit-learn is easy, but the method seems interesting. Have you worked on this in the meantime? |
Hmm it's been 4 years... |
Ok so the paper has 55 citations, is that correct? |
Any news on this? |
Actually this is the original paper: https://link.springer.com/article/10.1023/B:MACH.0000008084.60811.49 |
Any updates? |
Please check the discussion in PR #7910 |
Support Vector Data Description (SVDD) could be a nice enhancement to OneClassSVM implementation.
A technical implementation is described in this paper:
http://www.csie.ntu.edu.tw/~cjlin/papers/svdd.pdf
Source code compatible with libsvm is available here:
http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#libsvm_for_svdd_and_finding_the_smallest_sphere_containing_all_data
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