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Learning Bayesian Belief Network Classifiers: Algorithms and System. Scaling-Up Support Vector Machines Using Boosting Algorithm. Boosted Noise Filters for Identifying Mislabeled Data. Doctor of Philosophy (Computer Sciences) UNIVERSITY.
Using Analytic QP and Sparseness to Speed Training of Support Vector Machines.
Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
Optimizing the Induction of Alternating Decision Trees.
Bernhard Pfahringer and Geoffrey Holmes and Richard Kirkby.
Efficient Discovery of Functional and Approximate Dependencies Using Partitions.
Yk Huhtala and Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen.
Decomposition Methods for Linear Support Vector Machines.
Wei-Chun Kao and Kai-Min Chung and Lucas Assun and Chih-Jen Lin.
considerably faster than competing methods such as Sequential Minimal Optimization or the Nearest Point Algorithm.
Incremental Hill-Climbing Search Applied to Bayesian Network Structure Learning.