On Wed, Jan 25, 2012 at 6:00 PM, Olivier Grisel wrote:
>
> > Once you have clustered the unlabeled samples,
> > you can add, as extra features on the labeled samples,
> > the distance from each cluster center (e.g. computed
> > via RBF kernel).
> > Is that what you are suggesting?
>
> They are more
2012/1/25 Paolo Losi :
> Hi Oliver,
>
> your reply is very informative (as always :-) ).
> I've got a couple of question for you. See below...
>
> On Tue, Jan 24, 2012 at 1:57 PM, Olivier Grisel
> wrote:
>>
>> If you can cheaply collect unsupervised data that looks similar to
>> your training set
Hi Oliver,
your reply is very informative (as always :-) ).
I've got a couple of question for you. See below...
On Tue, Jan 24, 2012 at 1:57 PM, Olivier Grisel wrote:
>
> If you can cheaply collect unsupervised data that looks similar to
> your training set (albeit without the labels and in much
Which classifier have you tried? Are you sure you selected the best
hyper-parameters with GridSearchCV? Have your tried to normalize the
dataset? For instance have a look at:
http://scikit-learn.org/dev/modules/preprocessing.html
For very sparse data with large variance in the feature, you shou
Am 15.01.2012 19:45, schrieb Gael Varoquaux:
> On Sun, Jan 15, 2012 at 07:39:00PM +0100, Philipp Singer wrote:
>> The problem is that my representation is very sparse so I have a huge
>> amount of zeros.
> That's actually good: some of our estimators are able to use a sparse
> representation to spe
On Sun, Jan 15, 2012 at 07:39:00PM +0100, Philipp Singer wrote:
> The problem is that my representation is very sparse so I have a huge
> amount of zeros.
That's actually good: some of our estimators are able to use a sparse
representation to speed up computation.
> Furthermore the dataset is ske
Hey guys!
I am currently trying to use the best possible classifier for my task.
In my case I have regularly slightly more features than training
examples and overall about 5000 features. The problem is that my
representation is very sparse so I have a huge amount of zeros. The
labels range fr
