使用无限内核训练SVMTraining SVM with Indefinite Kernels |
|
课程网址: | http://videolectures.net/icml08_ye_tsvm/ |
主讲教师: | Jieping Ye |
开课单位: | 密歇根大学 |
开课时间: | 2008-08-06 |
课程语种: | 英语 |
中文简介: | 从许多应用程序生成的相似性矩阵可能不是半正的,因此不适合内核机器框架。在本文中,我们研究了具有不确定内核的训练支持向量机的问题。我们考虑一个正则化的SVM公式,其中不定核矩阵被视为对某些未知的半正定半径(代理核)的噪声观察,并且可以同时计算支持向量和代理核。我们提出了一种用于优化的半无限二次约束线性规划公式,可以迭代求解以找到全局最优解。我们进一步建议采用额外的修剪策略,其显着提高算法的效率,同时保持算法的收敛特性。此外,我们展示了拟议的公式和多核学习之间的密切关系。对一组基准数据集的实验证明了所提算法的效率和有效性。 |
课程简介: | Similarity matrices generated from many applications may not be positive semidefinite, and hence can't fit into the kernel machine framework. In this paper, we study the problem of training support vector machines with an indefinite kernel. We consider a regularized SVM formulation, in which the indefinite kernel matrix is treated as a noisy observation of some unknown positive semidefinite one (proxy kernel) and the support vectors and the proxy kernel can be computed simultaneously. We propose a semi-infinite quadratically constrained linear program formulation for the optimization, which can be solved iteratively to find a global optimum solution. We further propose to employ an additional pruning strategy, which significantly improves the efficiency of the algorithm, while retaining the convergence property of the algorithm. In addition, we show the close relationship between the proposed formulation and multiple kernel learning. Experiments on a collection of benchmark data sets demonstrate the efficiency and effectiveness of the proposed algorithm. |
关 键 词: | 相似性矩阵; 向量机; 正则化 |
课程来源: | 视频讲座网 |
最后编审: | 2019-04-21:lxf |
阅读次数: | 68 |