Titlebook: Machine Learning: ECML 2002; 13th European Confer Tapio Elomaa,Heikki Mannila,Hannu Toivonen Conference proceedings 2002 Springer-Verlag Be

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Revising Engineering Models: Combining Computational Discovery with Knowledgearning an improved model of a battery on the International Space Station from telemetry data. Our results suggest that this hybrid approach can reduce model development time and improve model quality.

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Variance Optimized Bagging number of binary classification problems from the UCI repository using a Support Vector Machine (SVM) as the base-classifier learning algorithm. Our results indicate that the proposed technique can consistently outperform Bagging and can dramatically improve the SVM performance even in cases where the Bagging fails to improve the base-classifier.

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Sparse Online Greedy Support Vector Regressionn a new input sample only if its feature space image is linearly independent of the images of previously admitted samples. We show that the algorithm implements a form of gradient ascent and demonstrate its scaling and noise tolerance properties on three benchmark regression problems.

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