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Novel algorithm for constructing support vector machine regression ensemble

机译:支持向量机回归集成的新算法

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摘要

A novel algorithm for constructing support vector machine regression ensemble is proposed. As to regression prediction, support vector machine regression(SVMR) ensemble is proposed by resampling from given training data sets repeatedly and aggregating several independent SVMRs, each of which is trained to use a replicated training set. After training, several independently trained SVMRs need to be aggregated in an appropriate combination manner. Generally, the linear weighting is usually used like expert weighting score in Boosting Regression and it is without optimization capacity. Three combination techniques are proposed, including simple arithmetic mean,linear least square error weighting and nonlinear hierarchical combining that uses another upper-layer SVMR to combine several lower-layer SVMRs. Finally, simulation experiments demonstrate the accuracy and validity of the presented algorithm.
机译:提出了一种构造支持向量机回归集成的新算法。对于回归预测,通过反复从给定的训练数据集中重采样并聚合几个独立的SVMR,提出了支持向量机回归(SVMR)集合,每个SVMR被训练为使用复制的训练集。训练后,需要以适当的组合方式汇总几个独立训练的SVMR。通常,线性加权通常像Boosting Regression中的专家加权分数一样使用,并且没有优化能力。提出了三种组合技术,包括简单的算术平均值,线性最小二乘误差加权和非线性分层组合,这些组合使用另一个上层SVMR来组合多个下层SVMR。最后,仿真实验证明了该算法的准确性和有效性。

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