论文标题
计算的决策权重和神经分类器的新学习算法
Computed Decision Weights and a New Learning Algorithm for Neural Classifiers
论文作者
论文摘要
在本文中，我们考虑了计算的可能性，而不是训练神经分类器的决策层权重。这种可能性以两种方式出现，这是由于做出适当的损失功能选择以及解决了受约束优化的问题。后一种配方为既简单又有功效的预定权重，从而实现了有希望的新学习过程。
In this paper we consider the possibility of computing rather than training the decision layer weights of a neural classifier. Such a possibility arises in two way, from making an appropriate choice of loss function and by solving a problem of constrained optimization. The latter formulation leads to a promising new learning process for pre-decision weights with both simplicity and efficacy.
