The Equivalence of Support Vector Machine and Regularization Neural Networks
作者:Péter András
摘要
We show in this brief paper the equivalence of the support vector machine and regularization neural networks. We prove both implication sides of the equivalence in a generally applicable way. The novelty lies in the effective construction of the regularization operator corresponding to a given support vector machine formulation. We give also a short introductory description of both neural network approximation frameworks.
论文关键词:approximation, equivalent neural networks, regularization, support vector machine
论文评审过程:
论文官网地址:https://doi.org/10.1023/A:1015292818897