mlsanityData to 14 Oct 2026

Top-k Multiclass SVM

arXiv 1511.06683 ↗First posted 20 Nov 2015stat.ML cs.CV cs.LGcited by 28

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Abstract (arXiv)

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.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Nov 2015.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 3; 2024: 7; 2025: 3.

28 papers.

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  1. 2022Sparsity-Constrained Optimal Transportcited by 32
  2. 2021Set-valued classification -- overview via a unified frameworkcited by 27
  3. 2021TkML-AP: Adversarial Attacks to Top-k Multi-Label Learningcited by 26
  4. 2022Rank-based Decomposable Losses in Machine Learning: A Surveycited by 26
  5. 2023Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspectivecited by 26
  6. 2021Sum of Ranked Range Loss for Supervised Learningcited by 23
  7. 2021sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classificationcited by 23
  8. 2024Cardinality-Aware Set Prediction and Top-k Classificationcited by 23
  9. 2022Differentiable Top-k Classification Learningcited by 21
  10. 2024Enhanced H-Consistency Boundscited by 18
  11. 2022An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogatescited by 14
  12. 2021Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivitycited by 11
16 more
  1. 2022Stochastic smoothing of the top-K calibrated hinge loss for deep imbalanced classificationcited by 10
  2. 2022Optimizing Partial Area Under the Top-k Curve: Theory and Practicecited by 9
  3. 2022Consistent Polyhedral Surrogates for Top-k Classification and Variantscited by 8
  4. 2025Why Ask One When You Can Ask k? Learning-to-Defer to the Top-k Expertscited by 7
  5. 2025Conformal Prediction for Long-Tailed Classificationcited by 7
  6. 2023Multi-class Support Vector Machine with Maximizing Minimum Margincited by 6
  7. 2021Fine-grained Generalization Analysis of Vector-valued Learningcited by 5
  8. 2024Top-k Classification and Cardinality-Aware Predictioncited by 5
  9. 2021Tensor Normalization and Full Distribution Trainingcited by 3
  10. 2025Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmapcited by 2
  11. 2022Provable Robust Saliency-based Explanationscited by 1
  12. 2024Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classificationcited by 1
  13. 2023A two-head loss function for deep Average-K classificationcited by 0
  14. 2024pSVM: Soft-margin SVMs with p-norm Hinge Losscited by 0
  15. 2024Detecci'on Autom'atica de Patolog'ias en Notas Cl'inicas en Espa~nol Combinando Modelos de Lenguaje y Ontolog'ias M'edicoscited by 0
  16. 2024Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologiescited by 0