title: Classification of handwritten digits using a RAM neural net architecture.
abstract: Results are reported on the task of recognizing handwritten digits without any advanced pre-processing. The result are obtained using a RAM-based neural network, making use of small receptive fields. Furthermore, a technique that introduces negative weights into the RAM net is reported. The results obtained on the task of recognizing handwritten digits is comparable with the best performances reported in the literature.
authors.affiliation: Optics & Fluid Dynamics Department, Risø National Laboratory, Roskilde, Denmark. thomas.martini@risoe.dk
authors.forename: T M
authors.initials: TM
authors.lastname: Jørgensen
mesh_terms: D002965:Classification; D006236:Handwriting; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results