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摘要翻译：
支持向量机（SVM）是用于分类、回归和密度估计等数据挖掘任务的常用工具。然而，原有的支持向量机（C-SVM）只考虑边缘上或边缘上数据点的局部信息。因此，C-SVM失去了鲁棒性。解决这个问题的一种方法是根据整个数据的分布平移（即不旋转或不改变形状的移动）超平面。但现有的工作只能适用于一维情况。本文提出了一种简单有效的SVM（GS-SVM）方法，将现有的SVM方法扩展到多维情况。我们的方法根据投影在超平面法向量上的数据分布平移超平面。与C-SVM相比，GS-SVM在多个数据集上具有更好的性能。
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英文标题：
《General Scaled Support Vector Machines》
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作者：
Xin Liu, Ying Ding, Forrest Sheng Bao
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Support Vector Machines (SVMs) are popular tools for data mining tasks such as classification, regression, and density estimation. However, original SVM (C-SVM) only considers local information of data points on or over the margin. Therefore, C-SVM loses robustness. To solve this problem, one approach is to translate (i.e., to move without rotation or change of shape) the hyperplane according to the distribution of the entire data. But existing work can only be applied for 1-D case. In this paper, we propose a simple and efficient method called General Scaled SVM (GS-SVM) to extend the existing approach to multi-dimensional case. Our method translates the hyperplane according to the distribution of data projected on the normal vector of the hyperplane. Compared with C-SVM, GS-SVM has better performance on several data sets.
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PDF链接：
https://arxiv.org/pdf/1009.5268
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