> First I would like to thank you all for this great and handy machine
> learning toolkit. Second, would it be of an interest to add an
> implementation for Kernel Partial Least Squares for the library?
Nelle Varoquaux (my sister, @NelleV on github) wants to do a Kernel CCA,
which is heavily relat
Hi,
Ok then, I will evaluate the classification result on the digits dataset
and compare it with the linearSVC and SVC with gaussian kernel. For
regression I will consider the Boston dataset, one remark here is what
error criteria should I use? square loss or do you suggest something else.
Regard
Hi Everybody,
I have some IR techniques implemented in Python, and I want to contribute
to sklearn. But I am having some trouble with sparse data:
https://github.com/dvro/scikit-learn/blob/instance_reduction/sklearn/instance_reduction/enn.py
Here is one of the techniques (I'll improve the style
2013/11/29 abdalrahman eweiwi :
> Well, Kernel partial least squares can be used for regression as well as for
> classification. It has been shown to work as good as Gaussian processes for
> regression on head pose estimation applications. See for example this CVPR
> paper
>
> http://iselab.cvc.ua
