I'm also forwarding this answer from Dr Samy Bengio who hasn't
subscribed to ai-geostats. His e-mail address is available at the end of
his e-mail.
Best regards
--
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
--- Begin Message ---
Hello,
My own contribution to the following question:
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.
It is always possible to use a regression technique to solve a classification
task, while the converse is in general much harder (although never impossible).
Now why one should use one technique instead of another is a much wider
question. First, one has to think of the criterion that is optimized by the
underlying technique and compare it to the criterion that is seeked in the
problem at hand. The better these criteria fit one to the other, the more
fitted will be the technique. For instance, using a mean-squared error criterion
when solving a classification task is not optimal, although it opens the door
to many possible techniques. For classification tasks, it is better to have
a criterion that minimizes the number of errors (if this is what is expected),
and possibly while maximizing the distance between the classes in the feature
space (the so-called margin). Hence, SVMs are a good choice for classification.
However, some regression techniques, while not minimizing the best criterion,
offers other advantages that may prove interesting for the problem at hand,
such as smoothness, stochastic training, etc.
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.
The border between problems is in general obvious: is the target of
your task in N or in R ? and if in N, are the elements ordered or not? these
two simple questions decide whether it is a regression or a classification
task (although you might also have other types of tasks such as density
estimation or ranking).
-----Original Message-----
From: seba [mailto:[EMAIL PROTECTED]
Sent: 30 August 2005 18:17
To: [email protected]
Subject: [ai-geostats] natural neighbor applied to indicator transforms
Dear list members
I would like to have some comments, suggestions or critics about the
following topic:
building a (preliminary) local uncertainty model of the spatial
distribution of discrete (categorical) variables by means of natural
neighbor interpolation method applied to indicator transforms.
From my perspective, interpolating indicator variables (well, at the
end an indicator variable is the probability of occurrence of a given
class) by means of a method like natural neighbor is an easy and quick
way to build a (preliminary) model of local uncertainty of the studied
properties, avoiding problems of order relation violations.
In my specific case I apply natural neighbor interpolation to indicator
transforms representing lithological classes in the same way in which
direct indicator kriging is applied. In this way, looking at the spatial
distribution of the probability of occurrence of lithologies (or at the
distribution of the lithological classes, if some classification
algorithm is applied) I can have a first idea of the spatial
distribution of lithologies. Clearly this method is utilized only as an
explorative and preliminary data analysis tool.
Thank you in advance for your replies.
S. Trevisani
----
Samy Bengio
Senior Researcher in Machine Learning.
IDIAP, CP 592, rue du Simplon 4, 1920 Martigny, Switzerland.
tel: +41 27 721 77 39, fax: +41 27 721 77 12.
mailto:[EMAIL PROTECTED], http://www.idiap.ch/~bengio
--- End Message ---
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