Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/738

 Title: Generalization by Computation Through Memory Authors: Gopych, Petro Keywords: GeneralizationGrandmother Model for VisionNeural Network Assembly Memory ModelOne-Step LearningLearning from one ExampleNeuron Receptive FieldBell-Shaped TuningSemi-Representation Issue Date: 2006 Publisher: Institute of Information Theories and Applications FOI ITHEA Abstract: Usually, generalization is considered as a function of learning from a set of examples. In present work on the basis of recent neural network assembly memory model (NNAMM), a biologically plausible 'grandmother' model for vision, where each separate memory unit itself can generalize, has been proposed. For such a generalization by computation through memory, analytical formulae and numerical procedure are found to calculate exactly the perfectly learned memory unit's generalization ability. The model's memory has complex hierarchical structure, can be learned from one example by a one-step process, and may be considered as a semi-representational one. A simple binary neural network for bell-shaped tuning is described. URI: http://hdl.handle.net/10525/738 ISSN: 1313-0463 Appears in Collections: Volume 13 Number 2

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