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MKLasso模型的算法与模型选择

, PP. 105-110

Keywords: Lasso,视觉原理,L1范数正则化,核方法

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Abstract:

在KLasso模型基础之上,引入多核函数与多核参数重新建立的一种更为广发的非线性的多核KLasso模型(MKLasso模型),采用基于梯度Boosting的思想的算法进行求解,并依据人类观察事物的一个基本特征,即人眼位于数据空间较近时能够看清细节,较远时只能够看清整体结构的特性设计了一种模型选择策略,通过实际的3个数据集设计6组试验,来验证该算法的有效性。模拟试验结果表明MKLasso模型的预测能力明显优于KLasso模型,其预测均方误差提高了10倍;该算法运行高效,抗噪声能力强,在参数选择方面又有一定自己的优势,可以直接选择核参数,算法大大降低了调试与运算时间。

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