实现线性二进制SVM(支持向量机) [英] Implementing a linear, binary SVM (support vector machine)

问题描述

在高维二进制数据(文本)的情况下，我想实现一个简单的SVM分类器，对此，我认为简单的线性SVM最好.我自己实现它的原因基本上是我想学习它的工作原理，所以使用库不是我想要的.

I want to implement a simple SVM classifier, in the case of high-dimensional binary data (text), for which I think a simple linear SVM is best. The reason for implementing it myself is basically that I want to learn how it works, so using a library is not what I want.

问题在于，大多数教程都采用了一个方程式，可以将其作为二次方程式"解决，但它们从未展示出实际的算法！因此，您能指出我可以学习的一个非常简单的实现，还是(更好地)指向一个通向实现细节的教程?

The problem is that most tutorials go up to an equation that can be solved as a "quadratic problem", but they never show an actual algorithm! So could you point me either to a very simple implementation I could study, or (better) to a tutorial that goes all the way to the implementation details?

非常感谢！

推荐答案

顺序最小优化(SMO)方法的一些伪代码可以在John C. Platt的论文中找到: 支持向量机的快速培训使用顺序最小优化 .还有SMO算法的Java实现，该Java实现是出于研究和教育目的而开发的( SVM-JAVA ).

Some pseudocode for the Sequential Minimal Optimization (SMO) method can be found in this paper by John C. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. There is also a Java implementation of the SMO algorithm, which is developed for research and educational purpose (SVM-JAVA).

解决QP优化问题的其他常用方法包括:

Other commonly used methods to solve the QP optimization problem include:

• 约束共轭梯度
• 内点法
• 活动集方法

但是请注意，需要一些数学知识才能理解这些内容(拉格朗日乘数，Karush–Kuhn–Tucker条件等).

But be aware that some math knowledge is needed to understand this things (Lagrange multipliers, Karush–Kuhn–Tucker conditions, etc.).

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