Artificial Intelligence Review - Special issue on lazy learning
Solving the quadratic programming problem arising in support vector classification
Advances in kernel methods
Optimal control by least squares support vector machines
Neural Networks
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A tutorial on ν-support vector machines: Research Articles
Applied Stochastic Models in Business and Industry - Statistical Learning
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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Examples-based controllers use historical data to evaluate local approximation models. Large data sets make it prohibitively expensive to evaluate the best control action in real time. Support vector machines (SVM) are known for their ability to identify the minimal set of data points needed to reconstruct an optimal decision surface. A successful application is presented: the simplification of a six-dimensional robotic controller. The SVM reduced the size of the data set to 5.3% of its original size while retaining 99.7% classification accuracy, thus leading the way to online implementation. The results indicate that SVM may be highly effective for the simplification of examples-based controllers.