Support Vector Method for Novelty Detection
Support Vector Method for Novelty Detection
复制标题
DOI：
--
复制
发表时间：
1999-11
期刊：
影响因子：
5.8
通讯作者：
B. Scholkopf;R. C. Williamson;Alex Smola;J. Shawe-Taylor;John C. Platt
中科院分区：
文献类型：
--
作者：
B. Scholkopf;R. C. Williamson;Alex Smola;J. Shawe-Taylor;John C. Platt
假设你得到了一个从基本概率分布P中提取的数据集，并且你想要估计一个简单的输入空间子集S，使得从P中提取的测试点位于S之外的概率等于某个先验指定的介于0和1之间的ν。我们提出了一种方法来接近这个问题，方法是试图估计一个函数f，该函数对S是正的，对补是负的。F的函数形式由关于潜在的小训练数据子集的核展开给出；它通过控制相关特征空间中的权重向量的长度来正则化。我们对算法的统计性能进行了理论分析。该算法是支持向量机算法在无标记数据情况下的自然扩展。
Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified ν between 0 and 1. We propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. We provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data.