Pelckmans K, Suykens J A K, De Moor B
IEEE Trans Neural Netw. 2007 May;18(3):917-20. doi: 10.1109/TNN.2007.891187.
This letter investigates a tight convex relaxation to the problem of tuning the regularization constant with respect to a validation based criterion. A number of algorithms is covered including ridge regression, regularization networks, smoothing splines, and least squares support vector machines (LS-SVMs) for regression. This convex approach allows the application of reliable and efficient tools, thereby improving computational cost and automatization of the learning method. It is shown that all solutions of the relaxation allow an interpretation in terms of a solution to a weighted LS-SVM.
本文研究了一种针对基于验证准则调整正则化常数问题的紧密凸松弛方法。涵盖了多种算法，包括岭回归、正则化网络、平滑样条以及用于回归的最小二乘支持向量机（LS - SVM）。这种凸方法允许应用可靠且高效的工具，从而降低计算成本并提高学习方法的自动化程度。结果表明，松弛问题的所有解都可以根据加权LS - SVM的解进行解释。
