【論文精讀】Learning Bounds for Importance Weighting
Learning Bounds for Importance Weighting 原論文地址：Learning Bounds for Importance Weighting Abstract 1 Introduction 現實世界中機器學習訓練數據和測試數據樣本的分佈會有偏差。一個常見的修正方法叫做importance weighting，它通過給不同的訓練樣本的帶價值賦予權重來平衡這種偏差。一
相關文章
- 1. 論文閱讀：Instance Weighting in Dialogue Systems
- 2. 【論文精讀】SVM for Clustering
- 3. Machine Learning & Deep Learning 論文閱讀筆記
- 4. 【論文精讀】DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval
- 5. 【論文精讀】Improving Simple Models with Confidence Profiles
- 6. 論文閱讀 Prototypical Networks for Few-shot Learning
- 7. 論文閱讀-Prototype Rectification for Few-Shot Learning
- 8. 論文閱讀——Dual Learning for Machine Translation
- 9. 論文閱讀-《Learning Deep Features for Discriminative Localization》
- 10. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 更多相關文章...
- • C# 文本文件的讀寫 - C#教程
- • C# 二進制文件的讀寫 - C#教程
- • JDK13 GA發佈：5大特性解讀
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 論文閱讀：Instance Weighting in Dialogue Systems
- 2. 【論文精讀】SVM for Clustering
- 3. Machine Learning & Deep Learning 論文閱讀筆記
- 4. 【論文精讀】DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval
- 5. 【論文精讀】Improving Simple Models with Confidence Profiles
- 6. 論文閱讀 Prototypical Networks for Few-shot Learning
- 7. 論文閱讀-Prototype Rectification for Few-Shot Learning
- 8. 論文閱讀——Dual Learning for Machine Translation
- 9. 論文閱讀-《Learning Deep Features for Discriminative Localization》
- 10. 論文閱讀：《Wide & Deep Learning for Recommender Systems》