The Strength of Weak Learnability
Machine Learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Boosting a weak learning algorithm by majority
Information and Computation
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Reducing bias in curve estimation by use of weights
Computational Statistics & Data Analysis
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Some Equivalences between Kernel Methods and Information Theoretic Methods
Journal of VLSI Signal Processing Systems
Bandwidth selection in kernel density estimators for multiple-resolution classification
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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Kernel density estimation is a commonly used approach to classification. However, most of the theoretical results for kernel methods apply to estimation per se and not necessarily to classification. In this paper we show that when estimating the difference between two densities, the optimal smoothing parameters are increasing functions of the sample size of the complementary group, and we provide a small simluation study which examines the relative performance of kernel density methods when the final goal is classification.A relative newcomer to the classification portfolio is "boosting", and this paper proposes an algorithm for boosting kernel density classifiers. We note that boosting is closely linked to a previously proposed method of bias reduction in kernel density estimation and indicate how it will enjoy similar properties for classification. We show that boosting kernel classifiers reduces the bias whilst only slightly increasing the variance, with an overall reduction in error. Numerical examples and simulations are used to illustrate the findings, and we also suggest further areas of research.