Abstract
The performance of supervised learners depends on the presence of a relatively large labeled sample. This paper proposes an automatic ongoing learning system, which is able to incorporate new knowledge from the experience obtained when classifying new objects and correspondingly, to improve the efficiency of the system. We employ a stochastic rule for classifying and editing, along with a condensing algorithm based on local density to forget superfluous data (and control the sample size). The effectiveness of the algorithm is experimentally evaluated using a number of data sets taken from the UCI Machine Learning Database Repository.
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Barandela, R., Juárez, M.: Ongoing learning for supervised pattern recognition. In: 14th Brazilian Symposium Computer Graphics and Image Processing, pp. 51–58 (2001)
Bensaid, A.M., Hall, L.O., Bezdek, J.C., Clarke, L.P.: Partially supervised clustering for image segmentation. Pattern Recognition 29, 859–871 (1996)
Blum, A.: Chawla.: Learning from labelled and unlabeled data using graph mincuts. In: 18th International Conference on Machine Learning, pp. 19–26 (2001)
Castelli, V., Cover, T.M.: On the exponential value of labeled samples. Pattern Recognition Letters 16, 105–111 (1995)
Dasarathy, B.V.: Adaptive decision systems with extended learning for deployment in partially exposed environments. Optical Engineering 34, 1269–1280 (1995)
Pascual, D., Pla, F., Sánchez, J.S.: Non Parametric Local Density-based Clustering for Multimodal Overlapping Distributions. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 671–678. Springer, Heidelberg (2006)
Vázquez, F., Sánchez, J.S., Pla, F.: A stochastic approach to Wilson’s editing algorithm. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 35–42. Springer, Heidelberg (2005)
Wilson, D.L.: Asymptotic properties of nearest neighbour rules using edited data. IEEE Trans. on Systems, Man and Cybernetics 2, 408–421 (1972)
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Vázquez, F.D., Sánchez, J.S., Pla, F. (2008). Learning and Forgetting with Local Information of New Objects. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_32
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DOI: https://doi.org/10.1007/978-3-540-85920-8_32
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