Andelić E, Schafföner M, Katz M, Krüger S E, Wendemuth A
Neural Comput. 2006 Dec;18(12):2928-35. doi: 10.1162/neco.2006.18.12.2928.
Sparse nonlinear classification and regression models in reproducing kernel Hilbert spaces (RKHSs) are considered. The use of Mercer kernels and the square loss function gives rise to an overdetermined linear least-squares problem in the corresponding RKHS. When we apply a greedy forward selection scheme, the least-squares problem may be solved by an order-recursive update of the pseudoinverse in each iteration step. The computational time is linear with respect to the number of the selected training samples.
研究了再生核希尔伯特空间（RKHSs）中的稀疏非线性分类和回归模型。Mercer核和平方损失函数的使用在相应的RKHS中产生了一个超定线性最小二乘问题。当我们应用贪婪前向选择方案时，最小二乘问题可以通过在每个迭代步骤中对伪逆进行顺序递归更新来解决。计算时间与所选训练样本的数量成线性关系。
