正则化最小二乘分类的AlignLoo模型选择方法

正则化最小二乘分类（RLSC）是一种基于二次损失函数的正则化网络，其推广能力受模型参数影响，传统的模型选择方法是耗时的参数网格搜索．为此，提出一种新颖的AlignLoo模型选择方法，其关键在于将核参数与超参数分开优化,即最大化核-目标配准以选择最优核参数，最小化RLSC的留一法误差的界以选择最优超参数．该方法效率高且不需验证样本，并在IDA数据集上进行了测试，结果表明方法有效．

AlignLoo Model Selection Approach for Regularized Least-squares Classification

Regularized Least-Squares Classification(RLSC) is a kind of kernel-based square loss regularization network,and its generalization performance is severely influenced by the setting of its kernel and hyper parameters.A novel approach named AlignLoo is presented for optimal parameters selection,and the key of this approach lies in tuning the kernel parameter and hyper parameter sequentially.First,the optimal kernel parameter is selected by maximizing kernel target alignment,and then the optimal hyper-parameter is determined via minimizing RLSC's leave-one-out error bound.The experimental results on IDA benchmark datasets demonstrate that the proposed approach is superior to traditional grid search method in that it is much more time efficient and it needs no independent validation set.