Actually, you are using nu=.5, which means you are expecting a novelty
detection rate up to 50%.
You definitely decrease it. With .5 the result will be fairly random .
Roberto
From: Pagliari, Roberto [mailto:[email protected]]
Sent: Monday, September 15, 2014 10:28 AM
To: [email protected]
Subject: Re: [Scikit-learn-general] Bug in one class svm
Did you try change the value of nu? Perhaps, it’s too large.
From: Pagliari, Roberto [mailto:[email protected]]
Sent: Monday, September 15, 2014 10:24 AM
To:
[email protected]<mailto:[email protected]>
Subject: Re: [Scikit-learn-general] Bug in one class svm
I have used it with all kernels and several values of gamma (including the
default) and never had any issue with it,
Roberto
From: Albert Thomas [mailto:[email protected]]
Sent: Monday, September 15, 2014 10:00 AM
To:
[email protected]<mailto:[email protected]>
Subject: Re: [Scikit-learn-general] Bug in one class svm
When using the rbf kernel, you should try with a gamma > 0. It seems that you
set it to 0.
Albert
2014-09-15 15:37 GMT+02:00 Luca Puggini
<[email protected]<mailto:[email protected]>>:
Hi,
there is no segmentation fault in the default settings.
Even if according to the original paper it can make sense to use OCSVM also
with not rbf kernel.
Maybe there is a bug in the polynomial kernel, I don't know.
Despite that also with the RBF kernel I am having some problems with the
frontier.
I have posted a question with my problem here (plot included)
http://stats.stackexchange.com/questions/115481/one-class-svm-strange-decision-boundary
I do not exclude that I am doing some mistakes. So please tell me if I am wrong.
Thanks a lot,
Luca
Hi Luca,
it segfaults?! Can you confirm that it also segfaults if you use the
default arguments? There is no plot so I cannot say anything about the
strange decision boundaries.
For my part, I've never used something else than a RBF kernel for a one
class svm; the RBF kernel has the nice property that all data points lie on
the surface of a hypersphere and thus the minimum enclosing ball is just
the hyperplane that separates those points and the origin with the max
distance to the origin.
2014-09-15 10:58 GMT+02:00 Luca Puggini <lucapuggio@...<mailto:lucapuggio@...>>:
>
> Hi,
> I am having some problems with the OneClassSVM function.
>
> Here you can see my file and the output.
> http://justpaste.it/h3pw
>
> I am sorry but I can not share the used data.
>
> I have experienced also other problems like strange decision boundaries.
>
> Can someone tell me if I am doing something wrong or if there is a problem
> in the function?
>
> Thanks,
> Luca
>
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