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COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Prediction of generalization ability in learning machines
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The nature of statistical learning theory
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Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Evolutionary Neuro-Fuzzy Approach to Recognize On-Line Arabic Handwriting
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A genetic-designed beta basis function neural network for multi-variable functions approximation
Systems Analysis Modelling Simulation - Special issue: Advances in control and computer engineering
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
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Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
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ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
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Pattern Recognition Letters
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ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
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Fuzzy Sets and Systems
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MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Contribution to the discrimination of the medieval manuscript texts: application in the palaeography
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
A one-layer recurrent neural network for support vector machine learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Approximation properties of fuzzy systems for smooth functions and their first-order derivative
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
Training multilayer perceptron classifiers based on a modified support vector method
IEEE Transactions on Neural Networks
A formal analysis of stopping criteria of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
SVM-Based Tree-Type Neural Networks as a Critic in Adaptive Critic Designs for Control
IEEE Transactions on Neural Networks
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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.