Nelson James D B, Damper Robert I, Gunn Steve R, Guo Baofeng
Information: Signals, Images, Systems Research Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
Neural Netw. 2009 Jan;22(1):49-57. doi: 10.1016/j.neunet.2008.09.016. Epub 2008 Nov 12.
Fourier-based regularisation is considered for the support vector machine classification problem over absolutely integrable loss functions. By invoking the modest assumption that the decision function belongs to a Paley-Wiener space, it is shown that the classification problem can be developed in the context of signal theory. Furthermore, by employing the Paley-Wiener reproducing kernel, namely the sinc function, it is shown that a principled and finite kernel hyper-parameter search space can be discerned, a priori. Subsequent simulations performed on a commonly-available hyperspectral image data set reveal that the approach yields results that surpass state-of-the-art benchmarks.
针对绝对可积损失函数上的支持向量机分类问题，考虑基于傅里叶的正则化方法。通过引入决策函数属于帕利 - 维纳空间这一适度假设，证明了分类问题可以在信号理论的背景下展开。此外，通过使用帕利 - 维纳再生核，即 sinc 函数，证明了可以先验地识别出一个有原则的有限核超参数搜索空间。随后在一个常用的高光谱图像数据集上进行的模拟表明，该方法产生的结果超过了当前的基准水平。
