An Analysis of Inference with the Universum
An Analysis of Inference with the Universum
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发表时间：
2007-12
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通讯作者：
Fabian H Sinz;O. Chapelle;Alekh Agarwal;B. Scholkopf
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作者：
Fabian H Sinz;O. Chapelle;Alekh Agarwal;B. Scholkopf
我们研究的模式分类算法，最近提出的Vapnik和同事。它建立在一个新的归纳原理之上，该原理假设除了正负数据之外，还有第三类数据可用，称为Universum。我们通过与Fisher判别分析和面向PCA以及投影子空间中的SVM（或等效地，与数据相关的简化内核）建立联系来分析算法的行为。我们还提供了实验结果。
We study a pattern classification algorithm which has recently been proposed by Vapnik and coworkers. It builds on a new inductive principle which assumes that in addition to positive and negative data, a third class of data is available, termed the Universum. We assay the behavior of the algorithm by establishing links with Fisher discriminant analysis and oriented PCA, as well as with an SVM in a projected subspace (or, equivalently, with a data-dependent reduced kernel). We also provide experimental results.