对于参数估计量的标准差,我们则是利用拔靴法来估计,其结果的表现也与仿真的标准差很接近。
Results show that the proposed weighted regression calibration method is the most efficient and that the standard errors estimated using a bootstrap procedure are satisfactory.
我们将讨论拔靴集成法与多模激发法,以及这两个演算法是如何成功的被运用。我们也将介绍近来运用与拔靴集成法相似的方法,结合支持向量机所做的一些案例。
We discuss bagging and boosting and suggest some plausible justification for their success. We also describe some recent work about combining SVMs in a way similar to bagging.
我们将讨论拔靴集成法与多模激发法,以及这两个演算法是如何成功的被运用。我们也将介绍近来运用与拔靴集成法相似的方法,结合支持向量机所做的一些案例。
We discuss bagging and boosting and suggest some plausible justification for their success. We also describe some recent work about combining SVMs in a way similar to bagging.
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