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A Hybrid projection-based and Radial Basis Function Architecture: Initial Values and Global Optimisation

机译:基于混合投影和径向基函数的体系结构:初始值和全局优化

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摘要

We introduce a mechanism for constructing and training a hybrid architecture of projection-based units and radial basis functions. In particular, we introduce an optimisation scheme which includes several steps and assures a convergence to a useful solution. During Network architecture construction and training, it is determined whether a unit should e removed or replaced. The resulting architecture Often has a smaller number of units compared with competing architectures. A specific overfitting from shrinkage of the RBF Radii is addressed by introducing a penalty on small radii. Classification and regression results are demonstrated on various benchmark Data sets and compared with several variants of RBF networks [1,2]. A striking performance improvement is achieved on the vowel data Set[3].
机译:我们介绍了一种构造和训练基于投影的单位和径向基函数的混合体系结构的机制。特别是,我们介绍了一种优化方案,其中包括几个步骤,并确保了对有用解决方案的收敛。在网络体系结构的构建和培训过程中,确定是否应卸下或更换一个单元。与竞争性架构相比,生成的架构通常具有较少的单元数量。 RBF半径收缩引起的特定过拟合通过对小半径引入惩罚来解决。分类和回归结果在各种基准数据集上得到了证明,并与RBF网络的几种变体进行了比较[1,2]。元音数据集[3]实现了惊人的性能提升。

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