处理大数据集的平均支持向量机Averaging Support Vector Machines for Processing Large Data Sets |
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课程网址: | http://videolectures.net/icml08_garcke_asvm/ |
主讲教师: | Jochen Garcke |
开课单位: | 柏林理工大学 |
开课时间: | 2008-09-01 |
课程语种: | 英语 |
中文简介: | 采用非线性内核的支持向量机(SVM)(Vapnik,1998)对大数据集的处理受到基础优化问题的数值解决方案技术的非线性缩放的影响。如果内核矩阵不能再存储在主存储器中,则这尤其有效,因此需要一次又一次地重新计算给定数据点上的内核评估。我们研究了一种允许处理更大数据集的简单方法:我们将大数据集分成许多较小的数据集,每个较小的数据集足够小以允许内核矩阵的缓存,并为每个数据学习支持向量机集。对于数据点的评估,我们只是简单地平均不同SVM的结果。 |
课程简介: | The handling of large data sets by support vector machines (SVMs)(Vapnik, 1998) employing a nonlinear kernel suffers from the non-linear scaling of the numerical solution techniques for the underlying optimisation problem. This is in particular valid if the kernel matrix cannot be stored in the main memory anymore and therefore the evaluation of the kernel on given data points needs to be recomputed again and again. We investigate a simple approach to allow the processing of larger data sets: We separate the large data set into a number of smaller ones, each small enough to allow the caching of the kernel matrix, and learn a support vector machine for each of these data sets. For the evaluation on data points we then just simply average the results of the different SVMs. |
关 键 词: | 非线性内核; 向量机; 大数据集 |
课程来源: | 视频讲座网 |
最后编审: | 2019-04-18:cwx |
阅读次数: | 88 |