Computers that learn vs . Users that learn : Experiments with adaptive e - mail agents Joachim Diederich *

The classification, selection and organization of electronic messages (e-mail) is a task that can be supported by a neural information processing system. The objective is to select those incoming messages for display that are most important for a particular user, and to propose actions in anticipation of the user´s decisions. The artificial neural networks (ANNs) extract relevant information from incoming messages during a training period, learn the response to the incoming message, i.e., a sequence of user actions, and use the learned representation for the proposal of user actions. We test the system by comparing simple recurrent networks (SRNs, Elman, 1990) and recurrent cascade correlation networks (RCC, Fahlman, 1991) by use of a sequence production task. The performance of both network architectures in terms of network size and learning speed for a given data set is examined. Our results show that (1) RCC generates smaller networks with better performance compared to SRNs and (2) learns significantly faster than SRNs.