Dear H.Mallinson and UAI people,
Have a look at my clustering page,
http://www.csse.monash.edu.au/~dld/cluster.html
I would recommend the algorithm, Snob, that I have been involved in:
http://www.csse.monash.edu.au/~dld/Snob.html , which is good with noisy
data (and is Bayesian). But, of course, have a look at the other offerings on
the http://www.csse.monash.edu.au/~dld/cluster.html page, some of which are
Bayesian.
The term "non-parametric Bayesian" seems to mean different things to different
people. Some people would say 'No' to your question by definition, and others
would say 'Yes'. I shall play it safe here and now, and I shall pass.
Regards. - David Dowe.
Dr. David Dowe, School of Computer Science and Software Eng.,
Monash University, Clayton, Victoria 3168, Australia [EMAIL PROTECTED]
Tel:+61 3 9905-5776 Fax:+61 3 9905-5146 http://www.csse.monash.edu.au/~dld/
http://www.csse.monash.edu.au/~dld/Snob.html
http://www.csse.monash.edu.au/~dld/cluster.html
And, at http://www.thehungersite.com/ , you can help feed the world.
> From [EMAIL PROTECTED] Thu May 11 01:53:29 2000
> Subject: [UAI] unsupervised learning.
> To: [EMAIL PROTECTED]
> Content-transfer-encoding: 7BIT
>
> I wish to ask about unsupervised learning.
> Can anyone recommend a resource for algorithms effective on
> low dimensional but noisy data?
> Fuzzy cmeans and EM have been ineffective. There is a suspicion that
> the Gaussian assumption is weak.
>
> Is there a reference for material on bayesian techniques in this area?
> Is there such a thing as a non-parametric bayesian technique?
>
> Apologies if this is the wrong place to post such a request.
