Department of Control, Automation and System Analysis, Saint Petersburg State Forest Technical University, Russia.
Department of Computer Science, Saint Petersburg State Electrotechnical University, Russia.
Neural Netw. 2015 Sep;69:99-110. doi: 10.1016/j.neunet.2015.05.004. Epub 2015 Jun 9.
A robust one-class classification model as an extension of Campbell and Bennett's (C-B) novelty detection model on the case of interval-valued training data is proposed in the paper. It is shown that the dual optimization problem to a linear program in the C-B model has a nice property allowing to represent it as a set of simple linear programs. It is proposed also to replace the Gaussian kernel in the obtained linear support vector machines by the well-known triangular kernel which can be regarded as an approximation of the Gaussian kernel. This replacement allows us to get a finite set of simple linear optimization problems for dealing with interval-valued data. Numerical experiments with synthetic and real data illustrate performance of the proposed model.
本文提出了一种稳健的单类分类模型，作为坎贝尔和贝内特（C-B）新颖性检测模型在区间值训练数据情况下的扩展。结果表明，C-B 模型中的线性规划对偶优化问题具有一个很好的性质，可以将其表示为一组简单的线性规划。本文还提出用著名的三角核函数来替代所得线性支持向量机中的高斯核函数，三角核函数可以看作是高斯核函数的一种近似。这种替换允许我们得到一组简单的线性优化问题，用于处理区间值数据。使用合成数据和真实数据的数值实验表明了所提出模型的性能。
