Article:  |
An Introduction to Artificial Intelligence | |
| Subject: | Neural nets aren't all supervised | |
| Date: | 2005-10-23 16:19:09 | |
| From: | TheBoyKen | |
| It's great to see an article discussing neural nets for people who are new to them; they certainly can be confusing initially. However the article seems to give the impression that neural nets can only work if you present them with the initial training patterns (i.e. "given this input pattern, I want you to give me back this output pattern") - indeed, there are whole swathes of neural architectures based upon unsupervised learning, that is, where you don't have to supply the information that needs to be learned up front. One such example is Kohonen SOMs (Self Organising Maps). Admittedly they're a bit trickier to explain than simple backpropagation nnets, but nonetheless they (and other unsupervised architectures) represent a vast body of nnet research, so I think they'd at least be worth a mention in case your readers get the impression that in order to use any nnet in a problem, you have to supply it a list of dos and don'ts. This isn't the case. | ||
Showing messages 1 through 2 of 2.
-
Neural nets aren't all supervised
2005-11-07 04:06:56 frankberger [View]
Beyond SOM, you can have unsupervised learning with other algorithms too. An extremely successfull approach was TD-lambda learning invented by Sutton. Thesauro applied this to Backgammon and for the first time there was an AI that played on expert niveau. Other programs adopted that approach and today most people agree that those program outplay any human. On the mac you have two implementations to look at: Bgblitz (www.bgblitz.com used pure TD-Lambda) written in Java (by me BTW) and GNU-BG (www.gnubg.org, uses a mixture of self-learning and supervised learning).
-
Neural nets aren't all supervised
2005-10-23 17:15:46 Matthew Russell |
[View]
Just checked and there's a tiny overview of SOMs on Wikipedia: http://en.wikipedia.org/wiki/Self-organizing_map
But you're right. There are tons of AI learning techniques out there and just about every one of them has about a dozen well known variations of one sort or another. I would again refer the interested reader to Mitchell's Machine Learning for some high-quality treatment of many of the fundamental ones.
