The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The Perceptron Algorithm with Uneven Margins
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Noise Tolerant Variants of the Perceptron Algorithm
The Journal of Machine Learning Research
Approximate maximum margin algorithms with rules controlled by the number of mistakes
Proceedings of the 24th international conference on Machine learning
The perceptron with dynamic margin
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Constant rate approximate maximum margin algorithms
ECML'06 Proceedings of the 17th European conference on Machine Learning
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We analyse perceptron-like algorithms with margin considering both the standard classification condition and a modified one which demands a specific value of the margin in the augmented space. The new algorithms are shown to converge in a finite number of steps and used to approximately locate the optimal weight vector in the augmented space. As the data are embedded in the augmented space at a larger distance from the origin the maximum margin in that space approaches the maximum geometric one in the original space. Thus, our procedures exploiting the new algorithms can be regarded as approximate maximal margin classifiers.