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来自「This complete matlab for neural network」· 文本 代码 · 共 43 行

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 06:38:31 2002)

I don't know how to translate it, maybe (Yuan(2) Xue(2) Xi(2)). But I can give
 you some ideas about it.

Suppose you have several classifiers c1,c2,..,ck (called base classifier) for 
a learning problem, each one will make a classification decision when seeing a
 query instance. Meta-learning technology tries to learn a meta-classifier c b
ased on c1-ck but outperform any signle base classifier.

There are several kinds of meta-learning strategy: combiner, arbiter, multi-le
vel. For example, combiner take the labels made by each base classifier and th
e real label as the training example of the meta-learner. The meta-learner can
 use any learning algorithm to learn from training examples.

Stacked generalization and stacked regression are a good meta-learning method 
which has been verified effective in many emperical study. The idea of stacked
 regression is like this: suppose there is a training instance q, each base le
arner ci makes a prediction fi(q) (Here we assume the classifier is for real v
alue, but it also works for categorical data), and we try to combine the base 
classifiers by weighting them. The prediction made by meta-learner is 
w1 * f1(q) + w2 * f2(q) + ... + wk * fk(q). w1, w2, ..., wk are the weights fo
r each base-classifier, and they sum up to 1. So the key problem here is how t
o learn the weights. Stacked regression uses linear least squares to do it bas
ed on non-negative weight assumption. Cross-validation is also important in it
.

Boosting and Bagging are other very famous meta-learning methods.

You can check the following site on this topic, which provides links to many g
ood papers.
http://www.ai.univie.ac.at/oefai/ml/metal/metal-bib.html

【 在 luchu 的大作中提到: 】
: 和和,菜鸟一个,不过要急着写论文,请高手帮忙.
: 

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