Understand
The standard approach to supervised classification involves the minimization of a log-loss as an upper bound to the classification error.
- While this is a tight bound early on in the optimization, it overemphasizes the influence of incorrectly classified examples far from the decision boundary.
- Updating the upper bound during the optimization leads to improved classification rates while transforming the learning into a sequence of minimization problems.
- In addition, in the context where the classifier is part of a larger system, this modification makes it possible to link the performance of the classifier to that of the whole system, allowing the seamless introduction of external constraints.
Built on
Prodding the roc curve: Constrained optimization of classifier performance
M. C. Mozer, R. H. Dodier, M. D. Colagrosso, C. Guerra-Salcedo, and R. H. Wolniewicz · 2001
Earlier work this paper cites.
A parallel mixture of svms for very large scale problems
R. Collobert, S. Bengio, and Y. Bengio · 2002
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The concave-convex procedure
A. L. Yuille and A. Rangarajan · 2003
Earlier work this paper cites.
Considering cost asymmetry in learning classifiers
F. R. Bach, D. Heckerman, and E. Horvitz · 2006
Earlier work this paper cites.
Similar
Trading convexity for scalability
R. Collobert, F. Sinz, J. Weston, and L. Bottou · 2006
Cited alongside, same era.
Robust support vector machine training via convex outlier ablation
L. Xu, K. Crammer, and D. Schuurmans · 2006
Cited alongside, same era.
Nonconvex online support vector machines
c. Ertekin, L. Bottou, and C. L. Giles · 2011
Cited alongside, same era.
Batch and online learning algorithms for nonconvex neyman-pearson classification
G. Gasso, A. Pappaioannou, M. Spivak, and L. Bottou · 2011
Cited alongside, same era.
Two high stakes challenges in machine learning
L. Bottou
Cited in the paper.
Then
A stochastic gradient method with an exponential convergence rate for finite training sets
N. Le Roux, M. Schmidt, and F. Bach · 2012
Later among the works it cites.
Counterfactual reasoning and learning systems: The example of computational advertising
L. Bottou, J. Peters, J. Quinonero-Candela, D. X. Charles, D. M. Chickering, E. Portugaly, D. Ray, P. Simard, and E. Snelson · 2013
Later among the works it cites.
Optimizing f-measures by cost-sensitive classification
S. P. Parambath, N. Usunier, and Y. Grandvalet · 2014
Later among the works it cites.
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