C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
On the Algorithmic Implementation of Stochastic Discrimination
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
International Journal of Knowledge Engineering and Data Mining
Symmetries from uniform space covering in stochastic discrimination
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Bagging model trees for classification problems
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Local additive regression of decision stumps
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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This paper consists of two parts, one theoretical, and one experimental. And while its primary focus is the development of a mathematically rigorous, theoretical foundation for the field of supervised learning, including a discussion of what constitutes a "solvable pattern recognition problem", it will also provide some algorithmic detail for implementing the general classification method derived from the theory, a method based on classifier combination, and will discuss experimental results comparing its performance to other well-known methods on standard benchmark problems from the U.C. Irvine, and Statlog, collections. The practical consequences of this work are consistent with the mathematical predictions. Comparing our experimental results on 24 standard benchmark problems taken from the U.C. Irvine, and Statlog, collections, with those reported in the literature for other well-known methods, our method placed 1st on 19 problems, 2nd on 2 others, 4th on another, and 5th on the remaining 2.