Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed.
最后讨论了离散尺度与小波核函数的构造，核函数选择与核参数学习。
On the other hand, some admissible support vector kernels are proposed, including three coordinates-transform kernels, a wavelet kernel and a scaling kernel.
在核函数的构造方面，我们提出了几种可容许的支撑矢量核，包括三种坐标变换核、子波核和尺度核函数。
And this paper classifies the popular methods, the main ideas are as follows: edge preserving, vector quantization, wavelet transform, interpolation kernel, Fractal technology and edge model.
本文对目前流行的各种方法进行了归类，主要思想有：边缘保持、矢量量化、小波变换、插值核、分形技术以及边缘模型。
First, by studying the producing kernel, equivalent positions of the relation between the convergence of partial sums of wavelet expansions at jumping points and producing kernel are given.
首先，通过对再生核的研究，给出了小波级数的部分和在跳跃间断点处的收敛性与再生核之间的关系的等价命题。
The results show that this algorithm can model input and output learning kernel of dynamic nonlinear system quickly, which is superior to other learning methods of wavelet network.
结果表明该算法能够对动态非线性系统的输入输出快速学习和建模，优于其它小波网络的学习算法。
The results show that this algorithm can model input and output learning kernel of dynamic nonlinear system quickly, which is superior to other learning methods of wavelet network.
结果表明该算法能够对动态非线性系统的输入输出快速学习和建模，优于其它小波网络的学习算法。
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