318.txt

This complete matlab for neural network

发信人: daniel (飞翔鸟), 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 18:48:28 2002), 站内信件

Note that meta-learning is not a same concept to combining classfiers,

or more general, ensembles. The term itself has some ambiguity, and

the machine learning community does not hold an agreement on it up

to now. Although some researchers include ensembles as the content

of meta-learning, there are researchers who do not agree. Now combining

classifiers is more popular to be regarded as a subdomain of ensemble

learning. As for meta-learning, a popularly accepted recognition is to

learn from learning, learn for learning, or learn on learning. Roughly

speaking, it means to get something from the learning, or capture the

nature of some specific learning paradigm, then use them to guide

future learning.

Some words written by Christophe Giraud-Carrier, a leading expert

on meta-learning, may help to understand the notion. Hope you enjoy it:

"Discovering new algorithms (or versions thereof) has occupied much of

the research of the past decade with reasonable success. Despite empirical

studies comparing various algorithms, however, much remains to be learned

about what makes a particular algorithm work well (or not) in a particular

domain. There is a need to formulate or acquire such meta-knowledge, and

make consistent use of it.

"Although the term meta-learning has been ascribed different meanings by

different researchers, ...... meta-learning is defined as any attempt to

learn from the learning process itself. The goal is to understand how

learning itself can become flexible and/or adaptable, taking into account

the domain or task under study."
    

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