Andy writes:
> I would venture that which one is better would depend on the nature of
> your data.
> Do you know the number of types beforehand? And do all types have 1000
> categories?
The number of Types is defined, however the number of categories keeps
increasing...but as I see it is un
[Mistakenly posted as separate thread before, please ignore previous post]
I see. Thank you for the advice. This was initial novice iteration of the
solution and needs improvement of course. In terms of which, in order to
keep the behaviour of the classifier consistent, instead of a single
cla
> Then you don't need a OneVsRestClassifier as OvR is the default
> strategy for SGD. You do need to put a maximum on the number of
> classes before you start learning, though.
I see. Thank you for the advice. This was initial novice iteration of the
solution and needs improvement of course. In t
>
> > 2) Will there be a interface for online learning in OnevsRestClassifier?
>
> If you care to implement it, then we're happy to accept a patch. Are
> you doing multi-label classification?
No I am primarily working on multiclass classification with constantly
increasing number of classes
--
1) This might sound like a basic question, but when performing a partial_fit
in SGD Classifiers, does the new data to train on have to be in the
categories that are already in the set? Or conversely, if I come across/ want
to add a few documents(in a document classifier example) in a new categor
On 10/29/2013 5:11 AM, Olivier Grisel wrote:
2013/10/23 j vickroy :
On 10/23/2013 10:18 AM, Andreas Mueller wrote:
FYI, the features I would use for the superpixel based approach would be
"color" histogramms (bag of words of channel intensities):
Reshape the images to (-1, 6) so you have lists
Some of these
> techniques may be available in scikit-image (http://scikit-image.org/).
>
> Multi-spectral image classification is something a friend of mine has been
> working on for a project - I will ask for some additional tips. This sounds
> like really cool stuff! If you end up wit
t we are not completely satisfied
with its performance and it is difficult/tedious to train.
I am starting to look for alternative approaches, but I am really a
complete novice at this. This is my first foray into ML; nevertheless,
it is very interesting to me!
-- jv
On 10/22/2013 02:3
On 10/22/2013 4:54 PM, Ankit Agrawal wrote:
Hi Jim,
What Joe said is correct when you want to label/classify images, since
classifying images by trying to find similarity of the test image with the
training images on pixel level would not work even if there is some ordinary
-- just what I need! --jv
Joe
On 22 Oct 2013, at 22:27, jim vickroy wrote:
On 10/22/2013 2:47 PM, Joseph Jacobs wrote:
Hey Jim,
From my (non-expert) perspective, performing classification
pixel-wise would not be ideal (please correct me if I am wrong). I
think the better way would be to
Oct 2013, at 22:27, jim vickroy wrote:
On 10/22/2013 2:47 PM, Joseph Jacobs wrote:
Hey Jim,
From my (non-expert) perspective, performing classification
pixel-wise would not be ideal (please correct me if I am wrong). I
think the better way would be to perform some sort of feature
extraction on
On 10/22/2013 2:47 PM, Joseph Jacobs wrote:
Hey Jim,
From my (non-expert) perspective, performing classification pixel-wise
would not be ideal (please correct me if I am wrong). I think the
better way would be to perform some sort of feature extraction on the
image (eg. SIFT, SURF, HOG, LBP
Hi,
Apologies if this is an inappropriate question for this forum.
I have a collection of (1024x1024) mono-chromatic images in which each
pixel is to be labeled as 1 of several categories (e.g., 10).
Furthermore, each mono-chromatic image was captured through several
filters (e.g., 5).
My
on't have any specific project idea, James Bergstra is looking for
> a student for his hyperparameter optimization project. See the thread "GSoC
> Proposal (was: optimization instead of brute force GridSearch)".
>
> Mathieu
>
>
> On Tue, Apr 16, 2013 at 5:20 AM, An
Hi,
my name is Jim Holmström and I am currently a postgraduate in machine
learning at KTH, Sweden (Bachelor in Engineering physics). I have been
interested in contributing to sklearn for a while now. The GSoC seems to be
a really good way to get started.
I currently have no proposals of my own
On 8/2/2012 8:27 AM, Brian Holt wrote:
> Thanks Jim,
>
> Could you try it again with
>
> X = np.array([[0]])
>
> Note the double "[" bracket - this is what causes the problem for me.
>
> Cheers
> Brian
Sorry about that.
>>> x = numpy.array([[0
On 8/2/2012 6:05 AM, Brian Holt wrote:
> Hi list,
>
> I'm refactoring the tree module to introduce lazy argsorting and my
> unit tests are failing with:
>
> Exception ValueError: ValueError(u'ndarray is not Fortran
> contiguous',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
>
> I
On 7/30/2012 7:41 AM, Gael Varoquaux wrote:
> Hi Jim,
>
> It is not possible for us to give a general advice: there is no universal
> classifier working for all datasets (this is known as the "no free lunch
> theorem).
>
> If you have a lot of training data, you can
Hi,
I recently discovered scikit-learn and it looks very impressive!
I have a project that may be able to make use of scikit-learn and help
me dispense with allot of custom code.
The task is to identify 8 categories of features on 1024x1024 Solar
images captured in 6 channels (wavelengths). A
19 matches
Mail list logo
