title: Pattern fusion in feature recognition neural networks for handwritten character recognition.
abstract: B. Hussain and M.R. Kabuka (1994) proposed a feature recognition neural network to reduce the network size of neocognitron. However, a distinct subnet is created for every training pattern. Therefore, a big network is obtained when the number of training patterns is large. Furthermore, recognition rate can be hurt due to the failure of combining features from similar training patterns. We propose an improvement by incorporating the idea of fuzzy ARTMAP in the feature recognition neural network. Training patterns are allowed to be merged, based on the measure of similarity among features, resulting in a subnet being shared by similar patterns. Because of the fusion of training patterns, network size is reduced and recognition rate is increased.
authors.affiliation: Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung.
authors.forename: S J
authors.initials: SJ
authors.lastname: Lee
authors.forename: H L
authors.initials: HL
authors.lastname: Tsai