Automatic growing of a hopfield style network during training for classification
UNSPECIFIED (1997) Automatic growing of a hopfield style network during training for classification. NEURAL NETWORKS, 10 (3). pp. 529-537. ISSN 0893-6080Full text not available from this repository.
Hopfield networks, a type of Recurrent Neural Network, may be used as a tool for classification by storing exemplars as memories. This method of using the Hopfield network for classification has certain shortcomings, such as the limits on the number of class exemplars that can be stored and the size of the connection matrix required when used for classification of images. This paper describes a method of growing a Hopfield style network for use in classification of patterns. The complete network that is grown consists of three networks in sequence with the middle network being a Sully recurrent Hopfield style network. The first network is a one layer feedforward network while the last network is simply a selector network which selects components from the terminal state of the recurrent network. The Hopfield style network grows automatically during training as additional nodes are required. The resulting network can be trained for the purpose of classifying bi-polar vectors. Connection matrices are determined using a modified Widrow-Hoff learning rule, such that the exemplars are attracted to exemplars or prototypes within the same class. An unclassified element is then classified by the class of its attractor No pre-processing is required to determine prototypes and all the training elements are used directly. A reduction in the number of arithmetic operations from order of magnitude n(2) to n takes place by growing the network rather than initialising the network to size n. The method is successfully applied to the classification of cervical cells for cancer detection and to the classification of diabetes patients recorded in the ''Pima Indians Diabetes Data Base''. Results depict how the network grows as learning takes place. (C) 1997 Elsevier Science Ltd.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
|Journal or Publication Title:||NEURAL NETWORKS|
|Publisher:||PERGAMON-ELSEVIER SCIENCE LTD|
|Number of Pages:||9|
|Page Range:||pp. 529-537|
Actions (login required)