[pymvpa] PCA transformation prior to SVM classification

[Yaroslav Halchenko]

your "rule of thumb" seems to be adequate... but of cause  it all
depends on many things, such as how many relevant features you have and
what kind of effects you are looking for.

On Mon, 29 Nov 2010, Thorsten Kranz wrote:

> Hi Jakob!

> Definitely! Many classifiers are not so good if the input is too high
> dimensional.

> It is hard to say where critical limits are, but due to my experiences
> I'd say, e. g. for SVM: some hundreds might be o.k., some thousands
> aren't. In these cases, you definitely should do a feature selection.

> Other classifiers behave differently, e.g. SMLR does some kind of
> Feature Selection per se.

> What do the experts think about critical numbers of inputs?

> Greetings,

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