Overview 概述

One of the most successful _classification _methods for both linear and nonlinear data. 同时适用于线性、非线性数据分类。
It uses a nonlinear mapping to transform the original training data into a higher dimension. 使用非线性图谱把训练数据转化为更高维数据。
With the new dimension, it searches for the linear optimal separating hyperplane (i.e., “decision boundary”). 利用超平面（决策平面）分割新的高纬度数据机。
With an appropriate nonlinear mapping to a sufficiently high dimension, data from two classes can always be separated by a hyperplane. 通过适当的非线性图谱把数据集映射到高维时，总能找到一个超平面分割开来两类数据。
SVM finds this hyperplane using support vectors (“essential” training tuples) and margins (defined by the support vectors). 通过支持向量和边界找到超平面。

Algorithms 算法

支持向量机的训练可以被分为线性可分情况和非线性可分情况。
Training of SVM can be distinguished by