機器學習中的稀疏性：一種信息提取的現代方法(英文版)（簡體書）

本書以資訊選擇為視角，全面介紹了稀疏性在機器學習中的應用。透過系統性的講解，讀者能夠深入理解資訊選擇對機器學習演算法性能的重要性以及基於稀疏性的資訊選擇方法。

CHAPTER 1 INTRODUCTION 1.1 Feature Selection 1.2 Transfer Learning 1.3 Outline CHAPTER 2 BACKGROUND 2.1 Notations 2.2 Single-Layer Autoencoder 2.3 Long-Short-Term Memory Network 2.4 S圖達 Selw. Learning 2.7 Hyperspectral Signal Analysis 2.8 Human Activity Recognition CHAPTER 3 FEATURE SELECTION 3.1 Vertical Federated Learning-Based Supervised Feature Selection 3.2 Supervised Hyperspectral Band Selection 3.3 Unsupervised Feature Selection with Data Structure Preservation CHV​​Pel​​Ur. .2 Few-Shot Learning -Based Cross-Domain Human Activity Recognition BIBLIOGRAPHY