Re: [boost] [gsoc 2008] Neural Networks library

From: Jeremy Maitin-Shepard (jbms_at_[hidden])
Date: 2008-03-24 11:06:32

Arash Partow <arash_at_[hidden]> writes:

> Hi Milan,
> Your proposal sounds like a "nice" idea, but wouldn't it be better to generalize
> your ANN library into a generic maximum likelihood estimation library - because
> at the end of the day that is what an ANN is, nothing more nothing less it would
> also provide the users of the library access to other estimation/modeling
> paradigms,
> which is a good thing.

I would not agree that ANNs are simply one MLE estimation technique.
For one thing, there is such a variety of techniques that all can be
considered "artificial neural networks" that I would be very hesitant to
make any such generalizations about them.

Although I agree that some specific instances of ANN techniques can be
described in statistical terminology (e.g. any minimization of squared
error can be referred to as maximizing the likelihood assuming the
output of the network specifies the mean of a Gaussian distribution, but
depending on the application, this may or may not be a useful insight),
ANN techniques are not all statistical in nature.

I do agree that it may be useful for Boost to have libraries that
provide a wide range of statistical facilities, but I'd say that ANNs
are really a rather separate thing for the most part.

-- 
Jeremy Maitin-Shepard