Marshall J A, Gupta V S
Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599-3175, USA.
Network. 1998 May;9(2):279-302.
A new way of measuring generalization in unsupervised learning is presented. The measure is based on an exclusive allocation, or credit assignment, criterion. In a classifier that satisfies the criterion, input patterns are parsed so that the credit for each input feature is assigned exclusively to one of multiple, possibly overlapping, output categories. Such a classifier achieves context-sensitive, global representations of pattern data. Two additional constraints, sequence masking and uncertainty multiplexing, are described; these can be used to refine the measure of generalization. The generalization performance of EXIN networks, winner-take-all competitive learning networks, linear decorrelator networks, and Nigrin's SONNET-2 network are compared.
提出了一种在无监督学习中测量泛化的新方法。该度量基于一种排他性分配或信用分配标准。在满足该标准的分类器中，输入模式被解析，以便每个输入特征的信用被排他性地分配给多个可能重叠的输出类别之一。这样的分类器实现了模式数据的上下文敏感全局表示。描述了另外两个约束条件，即序列掩码和不确定性复用；这些可用于完善泛化度量。比较了EXIN网络、胜者全得竞争学习网络、线性去相关器网络和尼格林的SONNET-2网络的泛化性能。
