Combination of independent kernel density estimators in classification
Mateusz Kobos · 2009
A new classification algorithm based on combination of two independent kernel density estimators per class is proposed. Each estimator is characterized by a different bandwidth parameter. Combination of the estimators corresponds to viewing the data with different ¿resolutions¿. The intuition behind the method is that combining different views on the data yields a better insight into the data structure; therefore, it leads to a better classification result. The bandwidth parameters are adjusted automatically by the L-BFGS-B algorithm to minimize the cross-validation classification error. Results of experiments on benchmark data sets confirm the algorithm's applicability.