Showing papers on "Convolutional neural network published in 1987"

Self-organizing neural network models for visual pattern recognition.

Abstract: Two neural network models for visual pattern recognition are discussed. The first model, called a “neocognitron”, is a hierarchical multilayered network which has only afferent synaptic connections. It can acquire the ability to recognize patterns by “learning-without-a-teacher”: the repeated presentation of a set of training patterns is sufficient, and no information about the categories of the patterns is necessary. The cells of the highest stage eventually become “gnostic cells”, whose response shows the final result of the pattern-recognition of the network. Pattern recognition is performed on the basis of similarity in shape between patterns, and is not affected by deformation, nor by changes in size, nor by shifts in the position of the stimulus pattern.