Re: [Scikit-learn-general] Scikit-learn for large datasets?

Hi, Helge, this ECML/PKDD paper [1] might be helpful in the case of semi-supervised learning. Sometimes ago me and one of the authors of [1] talked about implementing the algorithm in sklearn. I think now is a good time to mention it in the mailing list. I'm not sure if there is any online semi-s

Re: [Scikit-learn-general] (Deep learning) pre-proposal for (GSOC) 2013

Sorry, I didn't see Andy's note ;) On Fri, Apr 19, 2013 at 11:23 PM, amir rahimi wrote: > Hi, > I recommend Theano <http://deeplearning.net/software/theano/> if you want > to use python with GPU for deep learning. It is tightly integrated with > numpy > >

Re: [Scikit-learn-general] (Deep learning) pre-proposal for (GSOC) 2013

Hi, I recommend Theano if you want to use python with GPU for deep learning. It is tightly integrated with numpy Best, Amir On Thu, Apr 18, 2013 at 9:21 PM, Wei LI wrote: > @Andy What do you mean by "blackbox" algorithm? Does that mean something >

Re: [Scikit-learn-general] Combining TFIDF and LDA features

Have you done tests using some other classifiers such as gradient boosting which has a kind of internal feature selection? On Fri, Sep 14, 2012 at 5:36 PM, Andreas Müller wrote: > I'd be interested in the outcome. > Let us know when you get it to work :) > > > - Ursprüngliche Mail - > Von

Re: [Scikit-learn-general] GradientBoostingRegression loss function and Structured svm

In fact I wanted to estimate plane parameters for small patches using structured output prediction. But, my dataset is very noisy and I had not enough time to do that ( choosing kernels, parameters, cross validation and etc). I decided to estimate the depth at each point and smooth it by a CRF. As

Re: [Scikit-learn-general] GradientBoostingRegression loss function and Structured svm

n sklearn, it just > seems hard to integrate nicely. > > Btw, I have some structured SVM code to play around in Python, if you want: > > http://peekaboo-vision.blogspot.co.uk/2012/06/structured-svm-and-structured.html > > Cheers, > Andy > > > - Ursprüngliche Mail

[Scikit-learn-general] GradientBoostingRegression loss function and Structured svm

Hi all, I have two questions/requests Is there any way to define arbitrary loss function for gradient boosting regression? e.g. using huber penalty My request is about adding structured output prediction for SVM in the library. Is there any plan for adding that? -- ---