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PKDD
2010
Springer

Online Knowledge-Based Support Vector Machines

9 years 10 months ago
Online Knowledge-Based Support Vector Machines
Prior knowledge, in the form of simple advice rules, can greatly speed up convergence in learning algorithms. Online learning methods predict the label of the current point and then receive the correct label (and learn from that information). The goal of this work is to update the hypothesis taking into account not just the label feedback, but also the prior knowledge, in the form of soft polyhedral advice, so as to make increasingly accurate predictions on subsequent examples. Advice helps speed up and bias learning so that generalization can be obtained with less data. Our passive-aggressive approach updates the hypothesis using a hybrid loss that takes into account the margins of both the hypothesis and the advice on the current point. Encouraging computational results and loss bounds are provided.
Gautam Kunapuli, Kristin P. Bennett, Amina Shabbee
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where PKDD
Authors Gautam Kunapuli, Kristin P. Bennett, Amina Shabbeer, Richard Maclin, Jude W. Shavlik
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