Learning with local and global consistency閱讀報告NIPS2003
該論文被NIPS2003收錄，目前已被引用3011次，無疑是經典中的經典。提出了一種基於「smooth」理論的半監督學習方法，方法實現簡單、有效。 這裏所說的「smooth」是指：在半監督學習問題中，算法學習到的分類目標函數，相對於標籤樣本和無標籤樣本所共同顯示的內在結構，應該足夠平滑（smooth）。 算法基於兩個重要的假設：（1）空間中距離越近的點，越傾向於擁有同樣的標籤；（2）處於同一個結構
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相關文章
- 1. Global and Local Variables
- 2. FixMatch Simplifying Semi-Supervised Learning with Consistency and Confidence
- 3. Think Locally, Act Globally: Federated Learning with Local and Global Representations
- 4. HybridTime - Accessible Global Consistency with High Clock Uncertainty
- 5. FUSING DEEP LOCAL AND GLOBAL FEATURES FOR REMOTE SENSING IMAGE SCENE CLASSIFICATION閱讀筆記
- 6. node2vec: Scalable Feature Learning for Networks閱讀報告
- 7. 論文閱讀筆記《Few-Shot Learning with Global Class Representations》
- 8. 圖像拼接--Construction and Refinement of Panoramic Mosaics with Global and Local Alignment
- 9. 文獻閱讀報告 - Move, Attend and Predict
- 10. 半監督學習——FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence