【Key words】 neural network； genetic algorithm； gradient algorithm； statistical learning theory； support vector machine； regularization approach；
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lagrangian regularization approach 拉格朗日正则化方法
A deconvolution approach with an additive regularization term built around an minimal L1 norm is proposed.
文中在基于最小L1范数的加性调整条件下，提出了一种新的去卷积方法。
In traditional approach, people use l_2 -norm to measure data approximation item and regularization item in regularized image restoration and super-restoration in spatial domain.
在传统的空间域正则化图像复原或超分辨率图像复原方法中，是用向量2-范数度量数据逼近项和正则项。
A hybrid learning approach is presented in which genetic algorithms are used to optimize both the network architecture and the regularization coefficient.
提出了一种利用遗传算法优化前向神经网络的结构和正则项系数的混合学习算法。
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