Abstract
Support vector machines(SVM) have been introduced for pattern recognition and regression. But it was limited by the time consuming and the choice of kernel function in practical application. Motivated by the theory of multi-scale representations of signals and wavelet transforms, this paper presents a way for building a wavelet-based reproducing kernel Hilbert spaces (RKHS) which is a multiresolution scale subspace and its associate scaling kernel for least squares support vector machines (LS-SVM). The scaling kernel is constructed by using a scaling function with its different dilations and translations. Results on several approximation problems illustrate that the LS-SVM with scaling kernel can approximate arbitrary signal with multi-scale and owns better approximation performance.
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Xiangyang, M., Taiyi, Z., Yatong, Z. (2007). Scaling Kernels: A New Least Squares Support Vector Machine Kernel for Approximation. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_37
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DOI: https://doi.org/10.1007/978-3-540-76631-5_37
Publisher Name: Springer, Berlin, Heidelberg
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