stacked generalization什么意思

stacked generalization什么意思

Stacked generalization = 层叠泛化算法
是属于神经网络 (Neural Network) 领域的.
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Stacked generalization is a general method of using a high-level model to combine lowerlevel models to achieve greater predictive accuracy.In this paper we address two crucial issues which have been considered to be a `black art ' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert:the type of generalizer that is suitable to derive the higher-level model,and the kind of attributes that should be used as its input.We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms.
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Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set.This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it,and whose output is (for example) the correct guess.When used with multiple generalizers,stacked generalization can be seen as a more sophisticated version of cross-validation,exploiting a strategy more sophisticated than cross-validation 's crude winner-takes-all for combining the individual generalizers.When used with a single generalizer,stacked generalization is a scheme for estimating (and then correcting for) the error of a generalizer which has been trained on a particular learning set and then asked a particular question.After introducing stacked generalization and justifying its use,this paper presents two numerical experiments.
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