Research map / Label-efficient and robust learning / Recognition and long-tail
Active Learning of Multi-Class Classification Models from Ordered Class 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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| learn from ordered class sets (OCS) instead of class labels only (random selection) (ablation) | AMSVM · two synthetic and three real-world datasets | exact match rate | Helped |
| learn from OCS with EMC active learning vs label-only with EMC / uncertainty sampling (ablation) | AMSVM with EMC · five datasets | exact match rate | Helped |
| add EMC active learning instead of random selection (ablation) | CMSVM · five datasets | exact match rate | Helped |
| combine OCS and EMC active learning (ablation) | AMSVM · five datasets | exact match rate | Helped |
Closest papers by meaning
- Active Learning with Partial Feedback (ICLR 2019 · similarity 0.87)
- Active Sampling for Open-Set Classification without Initial Annotation (AAAI 2019 · similarity 0.87)
- Multi-class classification without multi-class labels (ICLR 2019 · similarity 0.86)
- Universal Rates for Active Learning (NeurIPS 2024 · similarity 0.84)
- Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation (ICML 2025 · similarity 0.83)
- Formal Models of Active Learning from Contrastive Examples (NeurIPS 2025 · similarity 0.83)
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.