Barnard E, Botha E C
Dept. of Comput. Sci. and Eng., Oregon Graduate Inst., Portland, OR.
IEEE Trans Neural Netw. 1993;4(5):794-802. doi: 10.1109/72.248457.
The ability of neural net classifiers to deal with a priori information is investigated. For this purpose, backpropagation classifiers are trained with data from known distributions with variable a priori probabilities, and their performance on separate test sets is evaluated. It is found that backpropagation employs a priori information in a slightly suboptimal fashion, but this does not have serious consequences on the performance of the classifier. Furthermore, it is found that the inferior generalization that results when an excessive number of network parameters are used can (partially) be ascribed to this suboptimality.
研究了神经网络分类器处理先验信息的能力。为此，使用具有可变先验概率的已知分布数据训练反向传播分类器，并评估它们在单独测试集上的性能。结果发现，反向传播以一种略次优的方式利用先验信息，但这对分类器的性能没有严重影响。此外，还发现当使用过多网络参数时导致的较差泛化能力（部分）可归因于这种次优性。
