Hi All,
I am trying to get started on the tutorial, but when entered from
mvpa2.tutorial_suite
import * I got the error message below. Does anyone know why this is
happening?
In [*8*]: from mvpa2.tutorial_suite import *
On Jan 23, 2014, at 6:03 PM, Payal Chakraborty wrote:
I am trying to get started on the tutorial, but when entered from
mvpa2.tutorial_suite import * I got the error message below. Does anyone
know why this is happening?
[...]
In [8]: from mvpa2.tutorial_suite import *
Hi Nick,
Thank you very much for your prompt response. I greatly appreciate all of
your help.
Best,
Payal
On Thu, Jan 23, 2014 at 12:15 PM, Nick Oosterhof
nikolaas.ooster...@unitn.it wrote:
On Jan 23, 2014, at 6:03 PM, Payal Chakraborty wrote:
I am trying to get started on the tutorial,
I have a question about trial averaging in MVPA, by which I mean taking the
average response of a certain stimulus class, and using this average value
as input to the classifier, instead of feeding it the responses from the
individual trials themselves.
For instance, in the original Haxby
Hi,
I'm not sure about the motivation for averaging in that particular paper
- if I had to guess, it might be that they chose a simple exposition to
present what at that time was a completely novel approach.
But averaging can work as a simple but effective method to improve the
signal/noise
On Jan 23, 2014, at 6:38 PM, Payal Chakraborty wrote:
Thank you very much for your prompt response. I greatly appreciate all of
your help.
You're welcome.
It turned out there was some offending code that could be safely removed, so I
just did that:
I agree with every one of Brian's points, and I'll toss one more in.
You' want to average if the analysis you want to do is to look at
similarity patterns, rather than train classifiers. It might have to
be combined with permutation tests (e.g. you average within things
labelled as being in the
Thanks Brian and Francisco.
Francisco, you said:
You' want to average if the analysis you want to do is to look at
similarity patterns, rather than train classifiers. It might have to
be combined with permutation tests (e.g. you average within things
labelled as being in the same class within
I also agree, and will toss in a few more ideas:
But forming decisions boundaries over features is exactly what a
classifier is meant to do, so why not just throw all these
different exemplars into the mix, and let the classifier figure out
its own notion of prototypicality?
I think because of
Hi, All
this might be unrelated to PyMVPA. but I am trying to find a good
implementation of clustering using Grown Neural Gas algorithm. I couldn't
find any information on PyMVPA. MDP offers growing neural gas class but it
is not implemented for fMRI data. anyone has any idea either finding a
I think a correlation classifier/method was used in Haxby's et al 2001 work,
and it gave high classification accuracy using the averages.
One might argue that, although not sure about this, assigning a volume/exemplar
to a single label/condition is problematic, thus, averaging is a good
I think a correlation classifier/method was used in Haxby's et al 2001 work,
and it gave high classification accuracy using the averages.
One might argue that, although not sure about this, assigning a volume/exemplar
to a single label/condition is problematic, thus, averaging is a good
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