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.
Regards
Eweiwi
> 2013/11/29 abdalrahman eweiwi <abdalrahman.ewe...@gmail.com>:
> > 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.uab.es/files/Publications/2012/PDF/AGD2012.pdf
> >
> > It can be used as a classification machine which I frequently use. See
> this
> > paper:
> >
> > http://enpub.fulton.asu.edu/cseml/06summer/pls_dis.pdf
>
> Interesting, thanks.
>
> > if you would like a brief report on its performance, I can run
> experiments
> > on the datasets you like and provide the results on its performance as a
> > classification machine, for both PLS and KPLS.
>
> It would be great to bench it quickly on the digits dataset from
> sklearn. It's a toy dataset that is not completely linearly separable.
> Please compare training speed and test accuracy using a 5 fold cross
> validation vs LinearSVC and SVC with a gaussian kernel with tuned
> values for C and gamma (assuming a rbf kernel for the SVC and kernel
> PLS model).
>
> You can also try to adapt the covertype benchmark to run your
> implementation of kernel PLS + linear svc on it:
>
>
> https://github.com/scikit-learn/scikit-learn/blob/master/benchmarks/bench_covertype.py
>
> But I am not sure that a kernel method can work on a dataset with that
> many samples.
>
> > For regression, I cant think right now of a benchmark for that, however,
> I
> > would be happy if you suggest any.
>
> That would be great indeed. In scikit-learn there is the boston house
> price regression dataset. Linear models do not perform as good as
> non-linear tree based models such as GBRT on this data.
>
> Before starting to write code for making kernel PLS part of the
> sklearn code base, please review the existing source code from the PLS
> estimator, the kernel PCA estimator and this PR to add kernel ridge:
> https://github.com/scikit-learn/scikit-learn/pull/2165
>
> There might be common code to reuse and / or refactor.
>
> Also make sure you read: http://scikit-learn.org/stable/developers/
>
> --
> Olivier
>
>
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