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Note: Links lead to the DBLP on the Web. Tapio Elomaa 19 Tapio Elomaa: Partition-Refining Algorithms for Learning Finite State Automata. ISMIS 2002 : 232-243 18 Tapio Elomaa, Juho Rousu : Linear-Time Preprocessing in Optimal Numerical Range Partitioning. JIIS 18 (1): 55-70 (2002) 17 Tapio Elomaa, Matti Kääriäinen : On the Practice of Branching Program Boosting. ECML 2001 : 133-144 16 Tapio Elomaa, Juho Rousu : Preprocessing Opportunities in Optimal Numerical Range Partitioning. ICDM 2001 : 115-122 15 Ilkka Autio , Tapio Elomaa, Teemu Kurppa : Robot Landmark Learning with SVMs. SCAI 2001 : 157-158 14 Tapio Elomaa, Juho Rousu : Generalizing Boundary Points. AAAI/IAAI 2000 : 570-576 13 Tapio Elomaa, Juho Rousu : On the Complexity of Optimal Multisplitting. ISMIS 2000 : 552-561 12 Tapio Elomaa: The Biases of Decision Tree Pruning Strategies. IDA 1999 : 63-74 11 Juho Rousu , Tapio Elomaa, Robert J. Aarts : Predicting the Speed of Beer Fermentation in Laboratory and Industrial Scale. IWANN (2) 1999 : 893-901 10 Tapio Elomaa, Juho Rousu : Speeding Up the Search for Optimal Partitions. PKDD 1999 : 89-97 9 Tapio Elomaa, Juho Rousu : General and Efficient Multisplitting of Numerical Attributes. Machine Learning 36 (3): 201-244 (1999) 8 Tapio Elomaa, Juho Rousu : Postponing the Evaluation of Attributes with a High Number of Boundary Points. PKDD 1998 : 221-229 7 Tapio Elomaa: A Review of How the Mind Works. AI Magazine 19 (3): 135 (1998) 6 Tapio Elomaa, Juho Rousu : Well-Behaved Evaluation Functions for Numerical Attributes. ISMIS 1997 : 147-156 5 Tapio Elomaa, Juho Rousu : Efficient Multisplitting on Numerical Data. PKDD 1997 : 178-188 4 Tapio Elomaa, Juho Rousu : On the Well-Behavedness of Important Attribute Evaluation Functions. SCAI 1997 : 95-106 3 Tapio Elomaa, Niklas Holsti , Ilkka Hyvärinen : TELA: A Platform for Experimenting with Attribute-based Learning Programs. SCAI 1995 : 392-395 2 Tapio Elomaa, Esko Ukkonen : A Geometric Approach to Feature Selection. ECML 1994 : 351-354 1 Tapio Elomaa: In Defense of C4.5: Notes Learning One-Level Decision Trees. ICML 1994 : 62-69 ![]() DiSC'02 © 2003 Association for Computing Machinery |