摘要
Support vector machines （SVMs） are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM （GDB-SVM）. It takes the distance between a point and classified hyperplane as classification rule,and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C ＋＋ 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all （OAA）. Three predicted results of five of GDB-SVM are better than those of the method of one against one （OAO）. Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO, but highly scalable for large data sets while generating high classification accuracy.
是因为 SVM 的训练复杂性高度依赖于设置的数据的尺寸，至于模式识别和机器学习为大规模数据采矿赞成了支持机器(SVM ) 不是的向量，这篇论文论述几何基于距离的 SVM (GDB-SVM ) 。Ittakes 在一个点和是的分类亢奋的飞机之间的距离分类根据理论分析和几何直觉统治，并且被设计。试验性的代码作为翻译并且编辑的系统是有 Microsoft Visual C++ 6.0 的导出的 fromLibSVM。五 GDB-SVM 的四预言的结果比对所有(OAA ) 的方法的那些好。五 GDB-SVM 的三预言的结果比对(天体观测卫星) 的方法的那些好。Experimentson 真实数据集合证明 GDB-SVM 比 OAA 和天体观测卫星的方法优异不仅，但是为大数据集合高度可伸缩当产生高分类精确性时。
