Hsia Jui-Yang, Lin Chih-Jen
IEEE Trans Neural Netw Learn Syst. 2020 Dec;31(12):5639-5644. doi: 10.1109/TNNLS.2020.2967637. Epub 2020 Nov 30.
In linear support vector regression (SVR), the regularization and error sensitivity parameters are used to avoid overfitting the training data. A proper selection of parameters is very essential for obtaining a good model, but the search process may be complicated and time-consuming. In an earlier work by Chu et al. (2015), an effective parameter-selection procedure by using warm-start techniques to solve a sequence of optimization problems has been proposed for linear classification. We extend their techniques to linear SVR, but address some new and challenging issues. In particular, linear classification involves only the regularization parameter, but linear SVR has an extra error sensitivity parameter. We investigate the effective range of each parameter and the sequence in checking the two parameters. Based on this work, an effective tool for the selection of parameters for linear SVR has been available for public use.
在线性支持向量回归（SVR）中，正则化参数和误差敏感度参数用于避免对训练数据的过度拟合。正确选择参数对于获得良好的模型至关重要，但搜索过程可能复杂且耗时。在Chu等人（2015年）的早期工作中，已经提出了一种通过使用热启动技术来解决一系列优化问题的有效参数选择程序用于线性分类。我们将他们的技术扩展到线性SVR，但解决了一些新的且具有挑战性问题。特别是，线性分类仅涉及正则化参数，而线性SVR有一个额外的误差敏感度参数。我们研究了每个参数的有效范围以及检查这两个参数的顺序。基于这项工作，一种用于线性SVR参数选择的有效工具已可供公众使用。
