Graph-Based Semi-Supervised Learning (eBook)

Amarnag Subramanya, Partha Pratim Talukdar (Autoren)

eBook Download: PDF

2014
125 Seiten
Morgan & Claypool Publishers (Verlag)
978-1-62705-202-3 (ISBN)

Infos

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

Produkt-Details

• Erscheint lt. Verlag: 1.7.2014
• Reihe/Serie: Synthesis Lectures on Artificial Intelligence and Machine Learning
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: semi-supervised learning, graph-based semi-supervised learning, manifold learning, graph-based learning, transductive learning, inductive learning, nonparametric learning, graph Laplacian, label propagation, scalable machine learning
• ISBN-10: 1-62705-202-X / 162705202X
• ISBN-13: 978-1-62705-202-3 / 9781627052023