A Process of Differentiation in the Assembly Neural Network

Goltsev, A. V.; Kussul, Ernst; Baidyk, Tatiana

doi:10.1007/978-3-540-30499-9_69

Cited by 3 publications

(1 citation statement)

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“…The present paper extends the study described in Refs. [16][17][18][19][20] and considers the assembly neural networks partitioned into separate sub-networks. These models have been applied to real-world tasks of texture segmentation and character recognition.…”

Section: Introductionmentioning

confidence: 99%

Generalization of Features in the Assembly Neural Networks

Goltsev

Wunsch

2004

Int. J. Neur. Syst.

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The purpose of the paper is an experimental study of the formation of class descriptions, taking place during learning, in assembly neural networks. The assembly neural network is artificially partitioned into several sub-networks according to the number of classes that the network has to recognize. The features extracted from input data are represented in neural column structures of the sub-networks. Hebbian neural assemblies are formed in the column structure of the sub-networks by weight adaptation. A specific class description is formed in each sub-network of the assembly neural network due to intersections between the neural assemblies. The process of formation of class descriptions in the sub-networks is interpreted as feature generalization. A set of special experiments is performed to study this process, on a task of character recognition using the MNIST database.

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Section: Introductionmentioning

confidence: 99%