Abstract
We present a new class of Perceptron-like algorithms with margin in which the “effective” learning rate η eff, defined as the ratio of the learning rate to the length of the weight vector, remains constant. We prove that for η eff sufficiently small the new algorithms converge in a finite number of steps and show that there exists a limit of the parameters involved in which convergence leads to classification with maximum margin. A soft margin extension for Perceptron-like large margin classifiers is also discussed.
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Tsampouka, P., Shawe-Taylor, J. (2006). Constant Rate Approximate Maximum Margin Algorithms. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Machine Learning: ECML 2006. ECML 2006. Lecture Notes in Computer Science(), vol 4212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871842_42
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DOI: https://doi.org/10.1007/11871842_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45375-8
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