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The top-$k$ error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels.
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Loss functions for top-k error: Analysis and insights
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Analysis and optimization of loss functions for multiclass, top-k, and multilabel classification
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Convexity , classification , and risk bounds
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Large margin classifiers: Convex loss, low noise, and convergence rates
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Statistical analysis of some multi-category large margin classification methods
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