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Knowledge Discovery in Databases: PKDD 2005, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings

, , , , and (Eds.) volume 3721 of Lecture Notes in Computer Science, Springer, (2005)

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Selecting parameters of SVM using meta-learning and kernel matrix-based meta-features., and . SAC, page 564-568. ACM, (2006)An Iterative Process for Building Learning Curves and Predicting Relative Performance of Classifiers., and . EPIA Workshops, volume 4874 of Lecture Notes in Computer Science, page 87-98. Springer, (2007)Reducing Rankings of Classifiers by Eliminating Redundant Classifiers., , and . EPIA, volume 2258 of Lecture Notes in Computer Science, page 14-21. Springer, (2001)Improved Dataset Characterisation for Meta-learning., , , and . Discovery Science, volume 2534 of Lecture Notes in Computer Science, page 141-152. Springer, (2002)Experimental Learning Model.. AISB/GI (ECAI), page 46-50. Leeds University, (1978)Learning Recursion with Iterative Bootstrap Induction (Extended Abstract)., and . ECML, volume 912 of Lecture Notes in Computer Science, page 299-302. Springer, (1995)Combining Feature and Algorithm Hyperparameter Selection using some Metalearning Methods., , and . AutoML@PKDD/ECML, volume 1998 of CEUR Workshop Proceedings, page 69-83. CEUR-WS.org, (2017)Simplifying the Algorithm Selection Using Reduction of Rankings of Classification Algorithms., , , and . ICSCA, page 140-148. ACM, (2019)Learning Text Extraction Rules, without Ignoring Stop Words., and . PRIS, page 128-138. INSTICC Press, (2004)A Method of Processing Unknown Attribute Values by ID3., and . ICCI, page 253-256. IEEE Computer Society, (1992)