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Abstract: We present a family of margin based online learning algorithms for various prediction tasks. In particular we derive and analyze algorithms for binary and multiclass categorization, regression, uniclass prediction and sequence prediction. The update steps of our different algorithms are all based on analytical solutions to simple constrained optimization problems. This unified view allows us to prove worst-case loss bounds for the different algorithms and for the various decision problems... (Update)
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0.6: Online Passive-Aggressive Algorithms - Crammer, Dekel, Shalev-Shwartz.. (2003) (Correct)
0.5: Kernel Methods for Missing Variables - Alex Smola Vishwanathan (2005) (Correct)
0.3: Online Learning with Kernels - Kivinen, Smola, Williamson (2002) (Correct)
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BibTeX entry: (Update)
@misc{ crammer-journal,
author = "Koby Crammer Crammer",
title = "Journal of Machine Learning Research 7 (2006) 551--585 Submitted 5/05;
Published 3/06 Online Passive-Aggressive Algorithms",
url = "citeseer.ist.psu.edu/757271.html" }
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31 The relaxation method for linear inequalities (context) - Agmon - 1954
28 the algorithmic implementation of multiclass kernel-based ve.. - Crammer, Singer - 2001
22 On convergence proofs on perceptrons (context) - Novikoff - 1962
17 The relaxed online maximum margin algorithm - Li, Long - 2002
16 Support vector machines for multi-class pattern recognition - Weston, Watkins - 1999
15 Hidden markov support vector machines - Altun, Tsochantaridis et al. - 2003
14 A new approximate maximal margin classification algorithm (context) - Gentile - 2001
12 A new family of online algorithms for category ranking - Crammer, Singer - 2000
12 Learning with Kernels: Support Vector Machines (context) - olkopf, Smola - 2002
11 Machine Learning - Gentile, of et al. - 2002
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5 Learning additive models online with fast evaluating kernels (context) - Herbster - 2001
2 IEEE Transactions on Signal Processing (context) - Kivinen, Smola et al. - 2002
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