313.txt

This complete matlab for neural network

发信人: lucky (乐凯), 信区: DataMining
标  题: Re: 什么是meta-learning?应如何翻译?
发信站: 南京大学小百合站 (Wed Apr 10 15:25:35 2002)

I am not working on AI either. Let me try to answer the questions from what I 
know:)
1. Yes, there are many previous works on this topic, and it is still an active
 research problem. Combining classifiers or stacking technique is only one kin
d of meta-learning. Boosting and Bagging have also been explored quite a lot.

2. The weights are not constants. They must be learned from the base-classifie
rs. That's why it's meta-learning. 
It's quite simple, which we often favor.
3. Optimality shouldn't be the objective. The accuracy of the meta-learner sho
uld be evaluated on some test data different from the training data.

another link to combining multiple classifier:
http://iris.usc.edu/Vision-Notes/bibliography/pattern566.html#Multiple%20Class
ifiers,%20Combining%20Classifiers,%20Combinations

【 在 fervvac 的大作中提到: 】
: Thanks for the essay. Together with daniel's authoratative explanation, the

: meta-learning in this example denotes to the process of learning the param..
: structure of a super-classifier given a set of classifiers.
: Just some quick comments:
: 1. I know littleb about AI, but I remember one faculty told me that the pr..
: of combining multiple classifiers has been extensively studied. And they w..
: well in practise, for a small amount of classifier. 
: 2. The weighted classifiers seem to be simple, or a little bit rough? I ex..
: w_k is not a constant. I doubt if a set of constant w_k will lead to 
: optimality (one can easily construct a counter-example). 
: 3. A related, but most important isue is how to evaluate the result, or if 

: optimality is achieved, what is the objective function?
: Just some provocative questions from layman, hoping to raise some network
: traffic to this board, :p. 
: 【 在 lucky (乐凯) 的大作中提到: 】
: : I don't know how to translate it, maybe (Yuan(2) Xue(2) Xi(2)). But I ca..
: :  you some ideas about it.
: : 
: : Suppose you have several classifiers c1,c2,..,ck (called base classifier..
: : a learning problem, each one will make a classification decision when se..
: (以下引言省略...)

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