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首页> 外文会议>International Conference on Signal Processing(ICSP'06); 20061116-20; Guilin(CN) >Unbiased Least Squares Support Vector Machine with Polynomial kernel
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Unbiased Least Squares Support Vector Machine with Polynomial kernel

机译：多项式核无偏最小二乘支持向量机

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摘要

Although least squares support vector machine (LS-SVM) has dramatically reduced the complexity of standard support vector machine (SVM), LS-SVM still costs too much time in tackling regression problems with large data sets. This paper presents an unbiased LS-SVM with inhomogeneous polynomial kernel, which shortens the training time of LS-SVM significantly without obvious loss of accuracy. This new LS-SVM is especially suitable for solving the large scale problems including relatively low dimensional input vectors. We also give an upper bound analytically. When its dimensionality is below the bound, a regression problem can be solved more efficiently by the new LS-SVM than by the standard one. The applications to a synthetic example and to an image interpolation problem show the efficiency of the new LS-SVM.
机译：尽管最小二乘支持向量机（LS-SVM）大大降低了标准支持向量机（SVM）的复杂性，但是LS-SVM在解决大型数据集的回归问题上仍然花费了太多时间。本文提出了一种具有不均匀多项式核的无偏LS-SVM，它在不明显降低精度的前提下，显着缩短了LS-SVM的训练时间。这种新的LS-SVM特别适合解决大规模问题，包括相对低维的输入向量。我们还分析地给出上限。当其维数在界限以下时，通过新的LS-SVM比通过标准LS-SVM可以更有效地解决回归问题。在合成示例和图像插值问题上的应用表明了新型LS-SVM的效率。

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