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首页> 外文会议>International Symposium on Neural Networks pt.1; 20040819-20040821; Dalian; CN >Distance-Based Selection of Potential Support Vectors by Kernel Matrix
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Distance-Based Selection of Potential Support Vectors by Kernel Matrix

机译：核矩阵的基于距离的潜在支持向量选择

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摘要

We follow the idea of decomposing a large data set into smaller groups, and present a novel distance-based method of selecting potential support vectors in each group by means of kernel matrix. Potential support vectors selected in the previous group are passed to the next group for further selection. Quadratic programming is performed only once, on the potential support vectors still retained in the last group, for the construction of an optimal hyperplane. We avoid solving unnecessary quadratic programming problems at intermediate stages, and can take control over the number of selected potential support vectors to cope with the limitations of memory capacity and existing optimizers' capability. Since this distance-based method does not work on data containing outliers and noises, we introduce the idea of separating outliersoises and the base, by use of the k-nearest neighbor algorithm, to improve generalization ability. Two optimal hyperplanes are constructed on the base part and the outlieroise part, respectively, which are then synthesized to derive the optimal hyperplane on the overall data.
机译：我们遵循将大数据集分解为较小的组的想法，并提出了一种新的基于距离的方法，该方法通过核矩阵选择每个组中的潜在支持向量。在上一组中选择的潜在支持向量将传递到下一组以进行进一步选择。二次编程仅对仍保留在最后一组中的潜在支持向量执行一次，以构建最佳超平面。我们避免在中间阶段解决不必要的二次编程问题，并且可以控制选定的潜在支持向量的数量，以应对内存容量和现有优化器功能的限制。由于这种基于距离的方法不适用于包含离群值和噪声的数据，因此我们引入了使用k最近邻算法将离群值/噪声与基数分离的想法，以提高泛化能力。在基础部分和离群值/噪声部分分别构建两个最优超平面，然后对它们进行合成以在整体数据上得出最优超平面。

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