Graph-Based Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied.

Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index

雖然標記的數據準備成本高昂，但越來越多的未標記數據正在廣泛提供。為了適應這一現象，已經開發了幾種半監督學習（SSL）算法，這些算法可以從標記和未標記的數據中學習。在另一個研究領域中，研究人員開始意識到圖形提供了一種在各種領域中表示數據的自然方式。基於圖形的SSL算法將這兩個研究領域結合在一起，已經在語音處理、計算機視覺、自然語言處理和其他人工智能領域的許多應用中表現出超越最新技術的性能。認識到這一有前途且新興的研究領域，本綜合講座專注於基於圖形的SSL算法（例如，標籤傳播方法）。我們希望讀者在閱讀本書後能夠獲得以下收穫：（1）對基於圖形的SSL算法的最新技術有深入了解，並能夠實施它們；（2）能夠判斷基於圖形的SSL方法對於解決問題的適用性；（3）熟悉基於圖形的SSL方法已成功應用的不同應用領域。

目錄：簡介/圖形構建/學習和推理/可擴展性/應用/未來工作/參考文獻/作者簡介/索引