Neural networks for classification and recognition of individual users in adaptive human-computer interface

The paper reports on a methodology for the identification of novel users from responses to computer-generated tasks. It is proposed that the cluster structure of the initial pool of data is identified. Standard neural algorithms are employed for classification according to the cluster structure. For recognition of patterns of data within each class, a compound network is employed that "individualizes" the patterns and stores them for subsequent recognition.