Growing ensemble of classifiers

Growing ensemble of linear classifiers uses the ´divide and conquer´ strategy in pattern recognition tasks. Performing the competitive learning the feature space is divided into subregions where linear classifiers are constructed. The structure of the ensemble is growing during the training and it is self determined. The overall output of the ensemble is the output of a winning member. The method is not bound to a specific type of classifier. According to the experimental results achieved on artificial and real world datasets the algorithm performs comparably to Gaussian SVM. Due to the simple nature of the decision boundary, knowledge retrieval procedures can be applied.