Perceptron Mistake Bounds
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Abstract (arXiv)
We present a brief survey of existing mistake bounds and introduce novel bounds for the Perceptron or the kernel Perceptron algorithm. Our novel bounds generalize beyond standard margin-loss type bounds, allow for any convex and Lipschitz loss function, and admit a very simple proof.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2013.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 0; 2025: 1.
5 papers.
- 2021Quantum Perceptron Revisited: Computational-Statistical Tradeoffscited by 5
- 2022MIME: Minority Inclusion for Majority Group Enhancement of AI Performancecited by 2
- 2025Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarkscited by 2
- 2021Margin-Independent Online Multiclass Learning via Convex Geometrycited by 0
- 2023Predictability of Machine Learning Algorithms and Related Feature Extraction Techniquescited by 0
- 2025Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks14 Apr 2025
- 2023Predictability of Machine Learning Algorithms and Related Feature Extraction Techniques30 Apr 2023
- 2022MIME: Minority Inclusion for Majority Group Enhancement of AI Performance1 Sep 2022
- 2021Margin-Independent Online Multiclass Learning via Convex Geometry15 Nov 2021
- 2021Quantum Perceptron Revisited: Computational-Statistical Tradeoffs4 Jun 2021