论文标题
随机机器回归方法：一个合奏支持向量回归模型，具有免费内核选择
Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice
论文作者
论文摘要
机器学习技术始终旨在减少广义预测错误。为了减少它，合奏方法提出了一种良好的方法，结合了几种模型，从而产生了更大的预测能力。随机机器已经被证明是强大的技术，即：高预测能力，用于分类任务，在本文中，我们提出了一种程序，以使用装袋加权的支持向量模型来回归问题。模拟研究是通过人工数据集和实际数据基准实现的。结果通过较低的概括误差表现出良好的回归随机机器，而无需在调整过程中选择最佳的内核函数。
Machine learning techniques always aim to reduce the generalized prediction error. In order to reduce it, ensemble methods present a good approach combining several models that results in a greater forecasting capacity. The Random Machines already have been demonstrated as strong technique, i.e: high predictive power, to classification tasks, in this article we propose an procedure to use the bagged-weighted support vector model to regression problems. Simulation studies were realized over artificial datasets, and over real data benchmarks. The results exhibited a good performance of Regression Random Machines through lower generalization error without needing to choose the best kernel function during tuning process.
