0


本地化多核学习

Localized Multiple Kernel Learning
课程网址: http://videolectures.net/icml08_gonen_lmk/  
主讲教师: Mehmet Gönen
开课单位: 阿尔托大学
开课时间: 2008-08-06
课程语种: 英语
中文简介:
最近,不是选择单个内核,而是提出了使用内核的凸组合的多内核学习(MKL),其中每个内核的权重在训练期间被优化。但是,MKL在整个输入空间中为内核分配相同的权重。在本文中,我们使用门控模型开发了一种局部多核学习(LMKL)算法,用于在本地选择适当的核函数。本地化选通模型和基于内核的分类器被耦合,并且它们的优化以联合方式完成。十个基准和两个生物信息学数据集的实证结果验证了我们方法的适用性。与MKL相比,LMKL通过存储更少的支持向量实现了统计上类似的准确度结果。 LMKL还可以组合位于不同部分的相同内核函数的多个副本。例如,具有多个线性内核的LMKL比在生物信息学数据集上使用单个线性内核提供更好的准确度结果。
课程简介: Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kernel over the whole input space. In this paper, we develop a localized multiple kernel learning (LMKL) algorithm using a gating model for selecting the appropriate kernel function locally. The localizing gating model and the kernel-based classifier are coupled and their optimization is done in a joint manner. Empirical results on ten benchmark and two bioinformatics data sets validate the applicability of our approach. LMKL achieves statistically similar accuracy results compared with MKL by storing fewer support vectors. LMKL can also combine multiple copies of the same kernel function localized in different parts. For example, LMKL with multiple linear kernels gives better accuracy results than using a single linear kernel on bioinformatics data sets.
关 键 词: 多内核学习; 门控模型; 核函数
课程来源: 视频讲座网
最后编审: 2019-04-18:cwx
阅读次数: 95