Abstract
We examine efficacy of a classifier based on average of kernel density estimators; each estimator corresponds to a different data “resolution”. Parameters of the estimators are adjusted to minimize the classification error. We propose properties of the data for which our algorithm should yield better results than the basic version of the method. Next, we generate data with postulated properties and conduct numerical experiments. Analysis of the results shows potential advantage of the new algorithm when compared with the baseline classifier.
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References
Specht, D.: Probabilistic neural networks. Neural Networks 3, 109–118 (1990)
Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Stat. Comput. 16, 1190–1208 (1995)
Kobos, M.: Combination of independent kernel density estimators in classification. In: Ganzha, M., Paprzycki, M. (eds.) International Multiconference on Computer Science and Information Technology, vol. 4, pp. 57–63 (2009)
Smyth, P., Wolpert, D.: Linearly combining density estimators via stacking. Mach. Learn. 36, 59–83 (1999)
Di Marzio, M., Taylor, C.C.: Boosting kernel density estimates: A bias reduction technique? Biometrika 91, 226–233 (2004)
Ormoneit, D., Tresp, V.: Averaging, maximum penalized likelihood and bayesian estimation for improving gaussian mixture probability density estimates. IEEE T. Neural. Networ. 9, 639–650 (1998)
Di Marzio, M., Taylor, C.C.: On boosting kernel density methods for multivariate data: density estimation and classification. Stat. Methods Appl. 14, 163–178 (2005)
Ghosh, A.K., Chaudhuri, P., Sengupta, D.: Classification using kernel density estimates: Multiscale analysis and visualization. Technometrics 48, 120–132 (2006)
Kobos, M., Mańdziuk, J.: Classification based on combination of kernel density estimators. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5769, pp. 125–134. Springer, Heidelberg (2009)
Ghosh, A.K., Chaudhuri, P.: Optimal smoothing in kernel discriminant analysis. Stat. Sinica 14, 457–483 (2004)
Wand, M.P., Jones, M.C.: Kernel Smoothing. Chapman and Hall, London (1995)
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Kobos, M., Mańdziuk, J. (2010). Classification Based on Multiple-Resolution Data View. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_16
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DOI: https://doi.org/10.1007/978-3-642-15825-4_16
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