Le 12 mars 2012 17:49, Robert Layton a écrit :
>
> I'll work off that template, and when I work out the details of the
> shrinking parameters (specifically which one is more in use), I'll branch
> and submit a PR.
Great. I think the nearest centroid is a very nice baseline classifier
for sanity c
On 13 March 2012 09:42, Olivier Grisel wrote:
> Le 11 mars 2012 20:35, Robert Layton a écrit :
> > Hi All,
> >
> > On reading some research, it appears that the shrunken centroid
> classifier
> > is one of the better methods for authorship analysis.
> > Therefore, I'm going to implement it at se
Le 11 mars 2012 20:35, Robert Layton a écrit :
> Hi All,
>
> On reading some research, it appears that the shrunken centroid classifier
> is one of the better methods for authorship analysis.
> Therefore, I'm going to implement it at see if it really is, and I was
> planning to add it to scikits.l
On Wed, Mar 07, 2012 at 09:48:32AM +0100, Gael Varoquaux wrote:
> There is a pull request that improves a lot the HMM implementation and
> documentation:
> https://github.com/scikit-learn/scikit-learn/pull/538
> It should be merged anytime.
It is merged :).
Speed of HMMs can probably be further
2012/3/12 Vikram Kamath :
> 1. Splits in CART are restricted to binary splits (a C4.5/C5.0 D-Tree is
> m-ary)
All our learners work on numeric data, meaning categorical data must
be split into binary features according to a one-of-K representation
prior to handing it to a learner.
So unless you
Hi,
This is in response to Peter and Adreas' queries about the differences between
CART and C4.5/C5.0
1. Splits in CART are restricted to binary splits (a C4.5/C5.0 D-Tree is m-ary)
2. Differences between C4.5/C5.0 and CART include differences in:
a. splitting criteria
b. the pruning meth
On 12 March 2012 19:30, Andreas wrote:
> **
> Hi Robert.
> To me, this sounds somwhat like Linear Discriminant Analysis or rather
> Quadratic Discriminant Analysis (without the shrinking part) to me.
>
> In these methods, a Gaussian is fitted to each class and classification
> is done by finding
Hi Robert.
To me, this sounds somwhat like Linear Discriminant Analysis or rather
Quadratic Discriminant Analysis (without the shrinking part) to me.
In these methods, a Gaussian is fitted to each class and classification
is done by finding the Gaussian that most likely created a data point.
Thi
