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Coupled Least Squares Support Vector Ensemble Machines

机译：耦合最小二乘支持向量集成机

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摘要

The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications. In this paper, we propose a novel coupled least squares support vector ensemble machine (C-LSSVEM). The proposed coupling ensemble helps improve robustness and produce good classification performance than the single model approach. The proposed C-LSSVEM can choose appropriate kernel types and their parameters in a good coupling strategy with a set of classifiers being trained simultaneously. The proposed method can further minimize the total loss of ensembles in kernel space. Thus, we form an ensemble regressor by co-optimizing and weighing base regressors. Experiments conducted on several datasets such as artificial datasets, UCI classification datasets, UCI regression datasets, handwritten digits datasets and NWPU-RESISC45 datasets, indicate that C-LSSVEM performs better in achieving the minimal regression loss and the best classification accuracy relative to selected state-of-the-art regression and classification techniques.
机译：最小二乘支持向量法是一种流行的数据驱动的建模方法，它显示出更好的性能，并已成功应用于广泛的应用中。在本文中，我们提出了一种新颖的耦合最小二乘支持向量集成机（C-LSSVEM）。与单模型方法相比，提出的耦合集成有助于提高鲁棒性并产生良好的分类性能。提出的C-LSSVEM可以在良好的耦合策略中选择合适的内核类型及其参数，同时对一组分类器进行训练。所提出的方法可以进一步最小化内核空间中合奏的总损失。因此，我们通过共同优化和加权基本回归来形成整体回归。对多个数据集（例如人工数据集，UCI分类数据集，UCI回归数据集，手写数字数据集和NWPU-RESISC45数据集）进行的实验表明，相对于所选状态，C-LSSVEM在实现最小回归损失和最佳分类精度方面表现更好，最先进的回归和分类技术。

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