Learning with nearest neighbour classifiers

Abstract

Premi extraordinari ex-aequo en l'àmbit d'Electrònica i Telecomunicacions. Convocatoria 1999 - 2000

Nearest Neighbour (NN) classifiers are one of the most celebrated algorithms in machine learning. In recent years, interest in these methods has flourished again in several fields (including statistics, machine learning and pattern recognition) since, in spite of their simplicity, they reveal as powerful non-parametric classification systems in real-world problems. The present work is mainly devoted to the development of new learning algorithms for these classifiers and is focused on the following topics:<br/><br/>- Development of learning algorithms for crisp and soft k-NN classifiers with large margin<br/>- Extension and generalization of Kohonen's LVQ algorithms<br/>- Local stabilization techniques for ensembles of NN classifiers<br/>- Study of the finite-sample convergence of the on-line LVQ1 and k-means algorithms<br/><br/>Besides, a novel oriented principal component analysis (OPCA) addressed for feature<br/>extraction in classification is introduced. The method integrates the feature extraction into the classifier and performs global training to extract those features useful for the classifier. The application of this general technique in the context of NN classifiers derives in a problem of learning their weight metric.