Re: Advantage

On 5 Oct 2003 19:56:06 -0700, [EMAIL PROTECTED] (William Siler) wrote:

>Dmitry A. Kazakov <[EMAIL PROTECTED]> wrote in message news:<[EMAIL PROTECTED]>...
>> On 1 Oct 2003 11:54:53 -0700, [EMAIL PROTECTED] (Bruno) wrote:
>> 
>> >Nobody has???
>> 
>> Neural networks provide a way to represent and organize data. Fuzzy
>> logic is an extension of the conventional logic. What is to compare
>> here? Absolutely nothing! A valid comparison could be neural vs. fuzzy
>> neural networks. But it would be just crisp vs. fuzzy.
>> 
>Actually, neural networks can provide a large number of functions,
>including (for example) classification. Thus for certain problems one
>can use either a neural net or a fuzzy expert system. They are, of
>course, quite different techniques.

I would disagree. A fuzzy expert system could be built on the basis of
a neuronal network. I mean, as the knowledge carrier one could take a
network instead of a data base of rules. From this point of view a
valid comparison could be: rules data base vs. neuronal network. And
again the word "fuzzy" have slipped away! (:-))

>An advantage of neural nets is that little or no a priori knowledge is
>required;

This only means that the learning algorithm is not tunable. It has no
parameters. Whether it is an advantage, is another question.

> the corresponding disadvantage is that after the neural net
>is constructed and tuned, one has little or no idea how it reaches its
>conclusion. There has been a lot of work into extracting a rule base
>that corresponds to the neural net from the neural net connections,
>but I have the impression that the results of this work are not
>terribly satisfactory.

Me too.

>However, I am not up to date on this work, so
>what I say may not be correct.
>
>A disadvantage of neural nets is that a training set of data and the
>corresponding conclusions is abolutely required. Some applications
>cannot possible meet this requirement.

After all it is a learning with a teacher, so it has its
disadvantages, like a necessity to have that "teacher".

>Expert systems do not usually require a training set of data for their
>construction; instead, expert kowledge is used to construct the rules.

Yes, but it again, it is comparing apples and oranges in my view.

>They do, of course, require calibration of model parameters and
>subsequent validation on real-life data. Neural nets also require
>validation.
>
>This is a very short treatment of the subject, but is perhaps better
>than nothing. I know of no publication that deals with the topic.

---
Regards,
Dmitry Kazakov
www.dmitry-kazakov.de