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首页> 外文会议>12th European Conference on Machine Learning, 12th, Sep 5-7, 2001, Freiburg, Germany >Applying the Bayesian Evidence Framework to ν-Support Vector Regression
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Applying the Bayesian Evidence Framework to ν-Support Vector Regression

机译：将贝叶斯证据框架应用于ν-支持向量回归

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摘要

Following previous successes on applying the Bayesian evidence framework to support vector classifiers and the ε-support vector regression algorithm, in this paper we extend the evidence framework also to the ν-support vector regression (ν-SVR) algorithm. We show that ν-SVR training implies a prior on the size of the e-tube that is dependent on the number of training patterns. Besides, this prior has properties that are in line with the error-regulating behavior of ν. Under the evidence framework, standard ν-SVR training can then be regarded as performing level one inference, while levels two and three allow automatic adjustments of the regularization and kernel parameters respectively, without the need of a validation set. Furthermore, this Bayesian extension allows computation of the prediction intervals, taking uncertainties of both the weight parameter and the ε-tube width into account. Performance of this method is illustrated on both synthetic and real-world data sets.
机译：继先前在将贝叶斯证据框架应用于支持向量分类器和ε-支持向量回归算法方面取得成功之后，本文将证据框架也扩展到ν-支持向量回归（ν-SVR）算法。我们表明，v-SVR训练意味着电子管的大小先验，这取决于训练模式的数量。此外，该先验的属性与ν的错误调节行为相符。在证据框架下，可以将标准ν-SVR训练视为执行一级推理，而二级和三级分别允许自动调整正则化和内核参数，而无需验证集。此外，该贝叶斯扩展允许考虑重量参数和ε管宽度的不​​确定性来计算预测间隔。在合成数据集和实际数据集上都说明了该方法的性能。

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