By Genebeck HAHN, Taekyoung KWON, Jooseok SONG
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Moreover , the decreas e in impurity achieved by splitting a node into two sub-nodes is not obtained as the difference between the total heterogeneity within the node and the het erogeneity within the two obtained sub-nodes. This is the reason why we suggest to construct classification trees measuring heterogeneity by referring to (8), which is a proper exte nsion of the mutual dispersion measure to the ordinal case, and definin g a criterion to select best splits on the basis of the difference between total and within het erogeneity.
FREUND , Y. and SCHAPIRE, R . (1996) : Experiments with a New Boosting Algorithm. Machin e Learning: Proceedings of the Thirteenth International Conf erence, July 1996. Proximity Measures Between Classification Trees 37 FREUND, Y. and SCHAPIRE, R. (1999): A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence, 14, 771-780. GORDON , A. D . (1999) : Classification, 2nd edition. Chapman and Hall/CRC, London. HUBERT, L. and ARABIE, P. (1985): Comparing Partitions.
Ap ) (Space Time Autoregressive Generalised) model (Di Giacinto 1994; Terzi 1995) P Y( ', t) = Ah LL
A Comparative Analysis of Extensible Authentication Protocols by Genebeck HAHN, Taekyoung KWON, Jooseok SONG