To answer to Gregoire's question, for some comparisons between SVM and
Indicator Kriging, here is a very basic paper (from 1999):
http://baikal-bangkok.org/~nicolas/publi/acai99-svm.pdf
and a thesis chapter (chapter 6), perhaps more interesting (from 2002):
http://baikal-bangkok.org/~nicolas/cartann/these_gilardi.pdf
My personnal feeling about the distinction between using a
classification algorithm or a regression one is the importance you put
on the boundaries.
If you look for smooth boundaries, with uncertainty estimations, etc.,
then a regression algorithm (like indicator kriging) is certainly a good
approach.
Now, if you don't care much about how the categories mix together at the
interface, or if you want clear decision boundaries, then a real
classification algorithm (like SVM) is certainly a better choice.
However, it is true that many algorithms can be used in either cases,
often with a small or no modification. The best examples are the
algorithms for density estimation (RBF, Parzen Windows...).
Algorithms of the category of SVM (i.e. large margin classifiers) are
interesting for classification because they are concentrating on finding
a separation between classes, not finding the "centre" of classes. In my
opinion, the interest of this technic for regression isn't obvious...
Best regards,
Nico
Gregoire Dubois wrote:
I recently attended a presentation about the mapping of soil properties.
Kriging was applied and I was wondering why a regression technique was
used instead of a classification algorithm.
Delineating soil properties seemed to be, at first sight, a
classification problem than a regression case. This was at first sight
and we didn't debate much on this issue unfortunately.
Indicator kriging (IK) is somehow a bridge between these two issues
(regression versus classification) and its simplicity in use and concept
makes it very attractive to solve many problems.
Now I wonder (again) if there are some fundamental papers comparing IK
to classification algorithms (e.g. Support Vector Machine, SVM). In the
same way, SVM used for regression seems to be not that uncommon as well.
So where is the borderline? When are we facing a classification problem
and when is it a regression problem? I am not sure the borderline is
always that obvious.
I am not answering Sebastiano's mail here but would be curious to see on
this list a debate on "regression versus classification"... I presume
there may there some material as well regarding the issue discussed below.
Best regards,
Gregoire
--
Nicolas Gilardi
Particle Physics Experiment group
University of Edinburgh, JCMB
Edinburgh EH9 3JZ, United Kingdoms
tel: +44 (0)131 650 5300 ; fax: +44 (0)131 650 7189
e-mail: [EMAIL PROTECTED] ; web: http://baikal-bangkok.org/~nicolas
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