A Classifier for Quantitative Feature Values Based on a Region Oriented Symbolic Approach
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Publisher
Springer Berlin Heidelberg
Link
http://link.springer.com/content/pdf/10.1007/978-3-540-30498-2_46
Reference8 articles.
1. Bock, H.H., Diday, E.: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data. Springer, Heidelberg (2000)
2. De Carvalho, F.A.T., Anselmo, C.A.F., Souza, R.M.C.R.: Symbolic approach to classify large data sets. In: Kiers, H.A.L., et al. (eds.) Data Analysis, Classification, and Related Methods, pp. 375–380. Springer, Heidelberg (2000)
3. Gowda, K.C., Krishna, G.: Agglomerative clustering using the concept of mutual nearest neighbourhood. Pattern Recognition 10, 105–112
4. Hand, D.J.: Construction and Assessment of Classification Rules. Willey, Chichester (1997)
5. Ichino, M., Yaguchi, H., Diday, E.: A fuzzy symbolic pattern classifier. In: Diday, E., et al. (eds.) Ordinal and Symbolic Data Analysis, pp. 92–102. Springer, Berlin (1996)
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