Hi everyone, First of: thanks to you for the rapid and enlightening replies.
On Thu, Nov 25, 2010 at 5:40 PM, Andreas Mueller <amuel...@ais.uni-bonn.de> wrote: > Hi there. > First of: I don't think this is a PyMVPA question. > There are some machine learning communities like > http://metaoptimize.com/qa/ and http://agbs.kyb.tuebingen.mpg.de/km/bb/. I'll post questions like these there in the future. Thanks for pointing me towards these lists. On Thu, Nov 25, 2010 at 6:22 PM, Emanuele Olivetti <emanu...@relativita.com> wrote: > About CSP... it is using class-labels. Isn't it? actually no, it does not use class labels. i had a look at the algorithm published in "2007 Liao Yao Wu Li Ieee Trans Biomed Engin Combining Spatial Filters for the Classification of Single-Trial EEG in a Finger Movement Task", (where they introduce the DSP as well). On Thu, Nov 25, 2010 at 11:27 PM, Yaroslav Halchenko <deb...@onerussian.com> wrote: > As you could see -- it would be just a memory-based classifier with > cheap train and expensive predict ;) Ok, got it. thanks for the nice explanation. >> >accuracy, i am looking at transformations (such as time-frequency >> >decomposition) on the data prior to feeding it to the classifier. > > btw, if you have pywt module available, you could give a try to > WaveletPacketMapper > and > WaveletTransformationMapper I'll have a look at it. For now i've been using eeglab and then reading in the transformed data to pymvpa. >> >PCA, CSP (common spatial patterns), DSP (discriminative spatial >> >patterns) and the like. >> As far as I know, PCA is mainly used to reduce the dimensionality and >> therefore the computational cost of the SVM. >> Since this is only a linear transform, I doubt that it will improve results. > > actually it depends... e.g. if underlying classifier's regularization is > invariant to the transformation (e.g. margin width), then yeap -- there should > be no effect. But if it is sensitive to it (e.g. feature selection , like in > SMLR), then you might get advantage since, like in the case of SMLR, the goal > of having fewer important features might be achieved with higher > generalization. A follow-up question; is the inverse true too: can having fewer important features lead to a higher generalization? best, jakob _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa