Hi Philipp,

you could try a nearest neighbors approach and use KL-divergence as
your "distance metric"**

best,
 Peter

** KL-divergence is not a proper metric but it might work

2012/5/14  <[email protected]>:
> I would try using a chi squared Kernel. You can Start by using the
> approximation provided in sklearn.
> Cheers, andy
> --
> Diese Nachricht wurde von meinem Android-Mobiltelefon mit K-9 Mail gesendet.
>
>
>
> Philipp Singer <[email protected]> schrieb:
>>
>> Hey there!
>>
>> I am currently trying to classify a dataset which has the following
>> format:
>>
>> Class1 0.3 0.5 0.2
>> Class2 0.9 0.1 0.0
>> ...
>>
>> So the features are probabilities that sum always up at exactly 1.
>>
>> I have tried several linear classifiers but I am now wondering if there
>> is maybe some better way to classify such data and achieve better results.
>>
>> Maybe someone has some ideas.
>>
>> Thanks and regards,
>> Philipp
>>
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>
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-- 
Peter Prettenhofer

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