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Top-k Multiclass SVM
Maksim Lapin · Matthias Hein · Bernt Schiele

Tue Dec 08 04:00 PM -- 08:59 PM (PST) @ 210 C #61

Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a direct method to optimize for top-k performance. Our generalization of the well-known multiclass SVM is based on a tight convex upper bound of the top-k error. We propose a fast optimization scheme based on an efficient projection onto the top-k simplex, which is of its own interest. Experiments on five datasets show consistent improvements in top-k accuracy compared to various baselines.

Author Information

Maksim Lapin (Max Planck Institute for Informatics)
Matthias Hein (Saarland University)
Bernt Schiele (Max Planck Institute for Informatics)

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