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An Exact Solver for the Weston-Watkins SVM Subproblem

arXiv 2102.05640 ↗First posted 10 Feb 2021stat.ML cs.LGcited by 0

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Abstract (arXiv)

Recent empirical evidence suggests that the Weston-Watkins support vector machine is among the best performing multiclass extensions of the binary SVM. Current state-of-the-art solvers repeatedly solve a particular subproblem approximately using an iterative strategy. In this work, we propose an algorithm that solves the subproblem exactly using a novel reparametrization of the Weston-Watkins dual problem. For linear WW-SVMs, our solver shows significant speed-up over the state-of-the-art solver when the number of classes is large. Our exact subproblem solver also allows us to prove linear convergence of the overall solver.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 49 references that are in the map. First posted on arXiv 10 Feb 2021.

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References · 6

  1. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
  2. 2017Self-Normalizing Neural Networkscited by 598
  3. 2017liquidSVM: A Fast and Versatile SVM packagecited by 7
  4. 2019Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classificationcited by 3
  5. 2017Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimizationcited by 2
  6. 2020Faster SVM Training via Conjugate SMOcited by 2

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