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Titlebook: Intelligent Information Systems 2002; Proceedings of the I Mieczys?aw A. K?opotek,S?awomir T. Wierzchoń,Macie Conference proceedings 2002 S

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樓主: retort
51#
發(fā)表于 2025-3-30 09:41:37 | 只看該作者
Zbigniew W. Ras,Shishir GuptaRückfall, die Fistel im Operationsgebiet, also an alter Stelle, unm?glich. Das Fistelgebiet — einschlie?lich der Fistelquelle — mu? zu einem Teil der ?u?eren Haut geworden sein (s.S. 71). Ist das geschehen, so kann es kein Rezidiv geben. In ganz seltenen F?llen, unter tausend F?llen einmal, hat sich
52#
發(fā)表于 2025-3-30 15:44:01 | 只看該作者
53#
發(fā)表于 2025-3-30 20:17:09 | 只看該作者
54#
發(fā)表于 2025-3-30 21:55:16 | 只看該作者
55#
發(fā)表于 2025-3-31 02:18:32 | 只看該作者
56#
發(fā)表于 2025-3-31 08:59:17 | 只看該作者
A Comparison of Six Discretization Algorithms Used for Prediction of Melanoma using criteria of rule set complexity, total number of errors, and expert’s evaluation. The best discretization method was based on divisive clustering technique. An additional experiment in which the best rules from all six rule sets, selected by an expert, were used for melanoma prediction, was a
57#
發(fā)表于 2025-3-31 12:35:44 | 只看該作者
Meta-learning via Search Combined with Parameter Optimization may outperform other algorithms on all data an almost optimal algorithm may be found within the SBM framework. To avoid tedious experimentation a meta-learning search procedure in the space of all possible algorithms is used to build new algorithms. Each new algorithm is generated by applying admis
58#
發(fā)表于 2025-3-31 14:30:35 | 只看該作者
Flexible Multidiscretizer Based on Measures which are Used in Induction of Decision Trees of learning time, a possible improvement of knowledge quality in case of noisy data, an increasing of knowledge legibility. In this paper we show analysis of a multidiscretizer which allows to apply a wide range of supervised discretization algorithms and which has very good abilities of tuning. De
59#
發(fā)表于 2025-3-31 19:27:10 | 只看該作者
Decision Tree Builder and Visualizer of decision tree generation and visualization. The system works with discrete and continuous attributes. DTB&V is a general tool allowing for: data preprocessing, generation of decision tree using developed algorithm, post processing (cutting the tree), and visualization of the obtained tree. DTB&V
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