Support Vector Neural Training.

Wlodzislaw Duch,
School of Computer Engineering, Nanyang Technological University, Singapore,
and Department of Informatics, Nicholas Copernicus University,
Grudziadzka 5, 87-100 Torun, Poland.


Neural networks are usually trained on all available data. Support Vector Machines start from all data but near the end of the training use only a small subset of vectors near the decision border. The same learning strategy may be used in neural networks, independently of the actual optimization method used. Feedforward step is used to identify vectors that will not contribute to optimization. Threshold for acceptance of useful vectors for training is dynamically adjusted during learning to avoid excessive oscillations in the number of support vectors. Benefits of such approach include faster training, higher accuracy of final solutions, identification of a small number of support vectors near decision borders, and efficient handling of classes with small number of vectors. Results on satellite image classification and hypothyroid disease obtained with this type of training are better than any other neural network results published so far.

Lecture Notes in Computer Science, Vol 3697 (2005) 67-72

Preprint for comments in PDF, 109 KB.

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