论文标题
基于内核化SVM的非线性分类器对问题进行排名
Nonlinear classifiers for ranking problems based on kernelized SVM
论文作者
论文摘要
许多分类问题的重点是仅在相关性最高而不是所有样本的样本上最大化性能。例如，我们可以提及排名问题，顶部的准确性或仅最重要的查询很重要的搜索引擎。在以前的工作中，我们得出了一个一般框架，其中包括这些线性分类问题的几类。在本文中，我们将框架扩展到非线性分类器。利用与SVM的相似性，我们将问题双重化，添加内核并提出了一种跨双重上升方法。
Many classification problems focus on maximizing the performance only on the samples with the highest relevance instead of all samples. As an example, we can mention ranking problems, accuracy at the top or search engines where only the top few queries matter. In our previous work, we derived a general framework including several classes of these linear classification problems. In this paper, we extend the framework to nonlinear classifiers. Utilizing a similarity to SVM, we dualize the problems, add kernels and propose a componentwise dual ascent method.
