Paper Info
Title
An Iterative Method for Deciding SVM and Single Layer Neural Network Structures
Abstract
We present two new classifiers for two-class classification problems using a new Beta-SVM kernel transformation and an iterative algorithm to concurrently select the support vectors for a support vector machine (SVM) and the hidden units for a single hidden layer neural network to achieve a better generalization performance. To construct the classifiers, the contributing data points are chosen on the basis of a thresholding scheme of the outputs of a single perceptron trained using all training data samples. The chosen support vectors are used to construct a new SVM classifier that we call Beta-SVN. The number of chosen support vectors is used to determine the structure of the hidden layer in a single hidden layer neural network that we call Beta-NN. The Beta-SVN and Beta-NN structures produced by our method outperformed other commonly used classifiers when tested on a 2-dimensional non-linearly separable data set.
Year
DOI
Venue
2011
10.1007/s11063-011-9171-3
Neural Processing Letters
Keywords
Field
DocType
Neural and statistical pattern recognition,Support vector machines,Single layer neural network,Kernel function,Beta function
Data point,Pattern recognition,Iterative method,Support vector machine,Artificial intelligence,Thresholding,Artificial neural network,Classifier (linguistics),Perceptron,Machine learning,Mathematics,Kernel (statistics)
Journal
Volume
Issue
ISSN
33
2
1370-4621
Citations 
PageRank 
References 
6
0.43
44
Authors
3
Name
Order
Citations
PageRank
Tarek M. Hamdani114316.16
Mohamed Adel Alimi21947217.16
Mohamed A. Khabou3849.90