On boosting kernel density methods for multivariate data: density estimation and classification |
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Authors: | Marco Di Marzio Charles C Taylor |
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Institution: | (1) Dipartimento di Metodi Quantitativi e Teoria Economica, Universitá G. d'Annunzio, Pescara, Italia;(2) Department of Statistics, University of Leeds , Leeds, UK |
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Abstract: | Statistical learning is emerging as a promising field where a number of algorithms from machine learning are interpreted as
statistical methods and vice-versa. Due to good practical performance, boosting is one of the most studied machine learning
techniques.
We propose algorithms for multivariate density estimation and classification. They are generated by using the traditional
kernel techniques as weak learners in boosting algorithms. Our algorithms take the form of multistep estimators, whose first
step is a standard kernel method. Some strategies for bandwidth selection are also discussed with regard both to the standard
kernel density classification problem, and to our 'boosted' kernel methods. Extensive experiments, using real and simulated
data, show an encouraging practical relevance of the findings. Standard kernel methods are often outperformed by the first
boosting iterations and in correspondence of several bandwidth values. In addition, the practical effectiveness of our classification
algorithm is confirmed by a comparative study on two real datasets, the competitors being trees including AdaBoosting with
trees. |
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Keywords: | Bandwidth selection bias reduction learning leave-one-out estimates simulation smoothing |
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