LEARNING BY NONSYMMETRIC KERNELS WITH DATA DEPENDENT SPACES AND
LEARNING BY NONSYMMETRIC KERNELS WITH DATA DEPENDENT SPACES AND
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DOI：
10.11650/tjm.14.2010.281
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发表时间：
2010-01
影响因子：
0.4
通讯作者：
Quan-Wu Xiao;Ding-Xuan Zhou
中科院分区：
文献类型：
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作者：
Quan-Wu Xiao;Ding-Xuan Zhou
研究了一种回归的学习算法。该算法是一种正则化方案，其中1个正则化表示在由非对称核从数据或样本训练的假设空间中。该算法的数据依赖性质导致了额外的误差项，称为假设误差，这与具有数据独立假设空间的正则化方案有本质的不同。通过处理正则化误差、样本误差和假设误差，我们根据回归问题的核、输入空间、边缘分布和回归函数的性质来估计总误差。学习速率是通过选择合适的正则化参数来获得的。在我们的数据依赖设置中使用了改进的错误分解方法。
We study a learning algorithm for regression. The algorithm is a regularization scheme with 1 regularizer stated in a hypothesis space trained from data or samples by a nonsymmetric kernel. The data dependent nature of the algorithm leads to an extra error term called hypothesis error, which is essentially different from regularization schemes with data independent hypothesis spaces. By dealing with regularization error, sample error and hypothesis error, we estimate the total error in terms of properties of the kernel, the input space, the marginal distribution, and the regression function of the regression problem. Learning rates are derived by choosing suitable values of the regularization parameter. An improved error decomposition approach is used in our data dependent setting.