Introduction Neural network models of autoassociative, distributed memory allow to be used storage and retrieval vector of many items where the number of stored items can exceed the vector dimension s can the number of neurons in the network This opens the possibility of a sublinear time search s (known in number of stored items) for approximate nearest neighbors vectors among high dimension.
Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension.