（DCL）Dynamic Curriculum Learning for Imbalanced Data Classification
Dynamic Curriculum Learning for Imbalanced Data Classification 動態課程學習用於樣本不均衡分類 對於數據不平衡，可以用重採樣和代價平衡（cost-sensitive），但它們需要先驗知識。爲了解決這一問題，本文提出DCL框架，用於每個batch中在線自適應調節採樣策略和損失學習，其泛化性能和判別能力更強。受CL啓發，DCL由兩級課程調度
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相關文章
- 1. Imbalanced Data
- 2. How to handle Imbalanced Classification Problems in machine learning?
- 3. Reinforcement Learning for Relation Classification from Noisy Data
- 4. (轉) Learning from Imbalanced Classes
- 5. 0429 SMT總結 Curriculum learning for MT
- 6. Becoming a Data Scientist – Curriculum via Metromap
- 7. Segmentation Guided Attention Network for Crowd Counting via Curriculum Learning
- 8. 讀文獻——《Curriculum learning》
- 9. Machine Learning for Encrypted Malware Traffic Classification
- 10. [導讀]Learning from Imbalanced Classes