Matrix analysis
COLT '90 Proceedings of the third annual workshop on Computational learning theory
The weighted majority algorithm
Information and Computation
Journal of the ACM (JACM)
Artificial Intelligence - Special issue on relevance
Machine Learning - Special issue on context sensitivity and concept drift
Machine Learning - Special issue on context sensitivity and concept drift
Linear hinge loss and average margin
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Numerical Recipes in Pascal: The Art of Scientific Computing
Numerical Recipes in Pascal: The Art of Scientific Computing
General Convergence Results for Linear Discriminant Updates
Machine Learning
The Relaxed Online Maximum Margin Algorithm
Machine Learning
Machine Learning
Machine Learning
Using upper confidence bounds for online learning
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Potential-Based Algorithms in On-Line Prediction and Game Theory
Machine Learning
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We introduce a variant of the Perceptron algorithm called second-order Perceptron algorithm, which is able to exploit certain spectral properties of the data. We analyze the second-order Perceptron algorithm in the mistake bound model of on-line learning and prove bounds in terms of the eigenvalues of the Gram matrix created from the data. The performance of the second-order Perceptron algorithm is affected by the setting of a parameter controlling the sensitivity to the distribution of the eigenvalues of the Gram matrix. Since this information is not preliminarly available to on-line algorithms, we also design a refined version of the second-order Perceptron algorithm which adaptively sets the value of this parameter. For this second algorithm we are able to prove mistake bounds corresponding to a nearly optimal constant setting of the parameter.