Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fitting additive models to regression data: diagnostics and alternative views
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Evolutionary product-unit neural networks classifiers
Neurocomputing
Prediction of street tree morphological parameters using artificial neural networks
Computers and Electronics in Agriculture
Feature extraction for nonlinear classification
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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A number of methods based on nonparametric regression have been developed in the last few years which are capable of approximating highly nonlinear class boundaries in classification problems. Bose (Comput. Statist. Data Anal. 22 (1996) 505) used additive splines for estimating the conditional class probabilities, and showed that the resulting method classification using splines (CUS) can achieve reasonably low misclassification error rates in many problems.This paper presents a powerful modification of CUS which we call the method of successive projections. This method can be used for any nonparametric regression based classification method but has been illustrated in this paper using mainly CUS, for simplicity and computational considerations. It seems to reduce the misclassification error rate of CUS in complex problems.