Research map / Learning theory and algorithms / Kernels and regression theory
Tight Margin-Based Generalization Bounds for Voting Classifiers over Finite Hypothesis Sets
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Which Algorithms Have Tight Generalization Bounds? (NeurIPS 2025 · similarity 0.87)
- Fantastic Generalization Measures are Nowhere to be Found (ICLR 2024 · similarity 0.86)
- Certification-Enhanced Generalization Bounds (NeurIPS 2026 · similarity 0.85)
- How many classifiers do we need? (NeurIPS 2024 · similarity 0.84)
- Tight Generalization Bounds for Large-Margin Halfspaces (NeurIPS 2025 · similarity 0.84)
- Exactly Tight Information-theoretic Generalization Bounds via Binary Jensen-Shannon Divergence (ICML 2025 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.