Similarity metric learning for a variable-kernel classifier
Neural Computation
Optimal linear combinations of neural networks
Neural Networks
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three learning phases for radial-basis-function networks
Neural Networks
Exact simplification of support vector solutions
The Journal of Machine Learning Research
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
An algorithm to cluster data for efficient classification of support vector machines
Expert Systems with Applications: An International Journal
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Orthogonal forward selection and backward elimination algorithms for feature subset selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reducing SVM classification time using multiple mirror classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
Analysis of input-output clustering for determining centers of RBFN
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
Fast orthogonal forward selection algorithm for feature subset selection
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neuron selection for RBF neural network classifier based on data structure preserving criterion
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
Reducing the computational load for training and classification procedures is a major problem in many pattern recognition approaches, such as artificial neural networks and support vector machines. Combining the multiple mirror classifiers is proven to be an efficient way to reduce the classification time. In this paper, we propose an approach that uses cooperative clustering method to construct mirror classifiers. With this procedure, the set of mirror point pairs with pre-determined size near the boundary of two classes is determined. Each mirror point pair constructs a small classifier. The minimum squared error based method and support vector machine based method are proposed to determine the weights for combining the multiple mirror classifiers. Experiments show that the training efficiency and classification efficiency are improved with a slight impact on generalization performance.