论文标题
支持向量机分类器的径向基函数内核优化
Radial basis function kernel optimization for Support Vector Machine classifiers
论文作者
论文摘要
支持向量机（SVM）仍然是最受欢迎和最精确的分类器之一。径向基函数（RBF）内核已在SVM中使用，以在相当成功的类别中分开。但是，对内核高参数的初始值有一个内在的依赖性。在这项工作中，我们提出了OKSVM，即一种自动学习RBF内核超参数并同时调整SVM权重的算法。提出的优化技术基于梯度下降方法。我们分析了有关经典SVM的方法的性能，以分类合成和真实数据。实验结果表明，OKSVM的性能更好，而不论RBF超参数的初始值。
Support Vector Machines (SVMs) are still one of the most popular and precise classifiers. The Radial Basis Function (RBF) kernel has been used in SVMs to separate among classes with considerable success. However, there is an intrinsic dependence on the initial value of the kernel hyperparameter. In this work, we propose OKSVM, an algorithm that automatically learns the RBF kernel hyperparameter and adjusts the SVM weights simultaneously. The proposed optimization technique is based on a gradient descent method. We analyze the performance of our approach with respect to the classical SVM for classification on synthetic and real data. Experimental results show that OKSVM performs better irrespective of the initial values of the RBF hyperparameter.
