《Cost-Effective Active Learning for Hierarchical Multi-Label Classification》--閱讀筆記-IJCAI
Authors Yi-Fan Yan , Sheng-Jun Huang Motivation 多標籤學習（multi-label learning，MLL）是一種實用而有效的學習框架，適用於語義複雜的對象，每個實例可以同時與多個類標籤相關聯。但當輸出空間遠遠大於單標籤學到的信息時，通常需要一個大的標籤數據集來訓練一個有效的MLL模型。此外，當有大量候選標籤時，多標籤對象的註釋成本可能非常高。主動
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