[IJCAI 2018筆記] Fast Factorization-Free Kernel Learning for Unlabeled Chunk Data Streams
針對無標註塊數據流的快速免分解核學習 文章目錄 #摘要 數據流分析旨在從連續到來的樣本中提取用於分類的判別信息。在以高效且穩定的方式更新模型的同時檢測新數據極具挑戰性，尤其是對於塊數據。本文提出了一種**快速免分解的核學習方法**，在一個框架中統一了無標註塊數據流的新穎性檢測和增量學習。所提出的方法通過求解**核空間中的線性系統**，從已知的**類中心構造聯合再生核Hilbert空間。自然，可以通
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相關文章
- 1. 論文閱讀筆記 | (IJCAI 2018 Oral) Collaborative Learning for Weakly Supervised Object Detection
- 2. 《Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data》閱讀筆記
- 3. 閱讀筆記 Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data
- 4. 論文理解Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
- 5. positive-unlabeled (PU) learning
- 6. Dynamic Hypergraph Structure Learning筆記（IJCAI-18）
- 7. 《Cost-Effective Active Learning for Hierarchical Multi-Label Classification》--閱讀筆記-IJCAI
- 8. Non-Sampling Learning for Personalized Recommendation——筆記
- 9. 《Mining Large Streams of User Data for Personalized Recommendations》筆記
- 10. Kernel Functions for Machine Learning Applications