How good are support vector machines?
Neural Networks
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
First-Order Tree-Type Dependence between Variables and Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Selection of Classifiers Based on Multiple Classifier Behaviour
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Trainable fusion rules. I. Large sample size case
Neural Networks
Trainable fusion rules. II. Small sample-size effects
Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A pool of classifiers by SLP: a multi-class case
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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While training single layer perceptron (SLP) in two-class situation, one may obtain seven types of statistical classifiers including minimum empirical error and support vector (SV) classifiers. Unfortunately, both classifiers cannot be obtained automatically in multi-category case. We suggest designing K(K-1)/2 pair-wise SLPs and combine them in a special way. Experiments using K=24 class chromosome and K=10 class yeast infection data illustrate effectiveness of new multi-class network of the single layer perceptrons.