The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
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发表时间：
1998-07
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通讯作者：
T. Frieß;N. Cristianini;C. Campbell
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作者：
T. Frieß;N. Cristianini;C. Campbell
支持向量机的工作原理是将分类任务的训练数据映射到高维特征空间。然后，他们在特征空间中找到分隔数据的最大边距超平面。通常使用二次编程例程来找到该超平面，该例程是计算密集型的，并且实现起来并不简单。在本文中，我们提出了一种 Adatron 算法的改进方案，用于高维空间中的核分类。该算法很简单，并且可以非常快速地找到解决方案，并且以指数级的快速收敛速度（以迭代次数计）趋向于最佳解决方案。提供了真实和人工数据集的实验结果。
Support Vector Machines work by mapping training data for classiication tasks into a high dimensional feature space. In the feature space they then nd a maximal margin hyperplane which separates the data. This hyperplane is usually found using a quadratic programming routine which is computation-ally intensive, and is non trivial to implement. In this paper we propose an adaptation of the Adatron algorithm for clas-siication with kernels in high dimensional spaces. The algorithm is simple and can nd a solution very rapidly with an exponentially fast rate of convergence (in the number of iterations) towards the optimal solution. Experimental results with real and artiicial datasets are provided.