Absolute error bounds for learning linear functions online
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Efficient learning of pseudo-boolean functions from limited training data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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We present an algorithm for the on-line learning of linear functions which is optimal to within a constant factor with respect to bounds on the sum of squared errors for a worst case sequence of trials. The bounds are logarithmic in the number of variables. Furthermore, the algorithm is shown to be optimally robust with respect to noise in the data (again to within a constant factor). We also discuss an application of our methods to the iterative solution of sparse systems of linear equations.