Abstract
A new classification algorithm based on combination of kernel density estimators is introduced. The method combines the estimators with different bandwidths what can be interpreted as looking at the data with different “resolutions” which, in turn, potentially gives the algorithm an insight into the structure of the data. The bandwidths are adjusted automatically to decrease the classification error. Results of the experiments using benchmark data sets show promising performance of the proposed approach when compared to classical algorithms.
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Kobos, M., Mańdziuk, J. (2009). Classification Based on Combination of Kernel Density Estimators. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_13
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DOI: https://doi.org/10.1007/978-3-642-04277-5_13
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