Large margin methods for structured classification : Exponentiated gradient algorithms and PAC-Bayesian generalization bounds
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Ben Taskar | Peter L. Bartlett | Michael Collins | David A. McAllester | David McAllester | P. Bartlett | B. Taskar | M. Collins
[1] Sôichi Kakeya,et al. On Differential Inequalities , 1918 .
[2] J. Lamperti. ON CONVERGENCE OF STOCHASTIC PROCESSES , 1962 .
[3] Manfred K. Warmuth,et al. Exponentiated Gradient Versus Gradient Descent for Linear Predictors , 1997, Inf. Comput..
[4] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[5] Michael Collins,et al. Parameter Estimation for Statistical Parsing Models: Theory and Practice of , 2001, IWPT.
[6] Koby Crammer,et al. On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..
[7] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[8] Ben Taskar,et al. Max-Margin Markov Networks , 2003, NIPS.
[9] David A. McAllester. Simplified PAC-Bayesian Margin Bounds , 2003, COLT.
[10] John Shawe-Taylor,et al. PAC Bayes and Margins , 2003 .
[11] Manfred K. Warmuth,et al. Relative Loss Bounds for Multidimensional Regression Problems , 1997, Machine Learning.