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…mehr
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
Produktdetails
Produktdetails
Synthesis Lectures on Artificial Intelligence and Machine Learning
Amarnag Subramanya is a Staff Research Scientist in the Natural Language Processing group at Google Research. Amarnag received his Ph.D. (2009) from the University of Washington, Seattle, working under the supervision of Jeff Bilmes. His dissertation focused on improving the performance and scalability of graph-based semi-supervised learning algorithms for problems in natural language, speed, and vision. Amarnags research interests include machine learning and graphical models. In particular, he is interested in the application of semi-supervised learning to large-scale problems in natural language processing. He was the recipient of the Microsoft Research Graduate fellowship in 2007. He recently co-organized a session on ""Semantic Processing"" at the National Academy of Engineerings (NAE) Frontiers of Engineering (USFOE) conference.Partha Pratim Talukdar is an Assistant Professor in the Supercomputer Education and Research Centre (SERC) at the Indian Institute of Science (IISc), Bangalore. Before that, Partha was a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University, working with Tom Mitchell on the NELL project. Partha received his Ph.D. (2010) in CIS from the University of Pennsylvania, working under the supervision of Fernando Pereira, Zack Ives, and Mark Liberman. Partha is broadly interested in Machine Learning, Natural Language Processing, Data Integration, and Cognitive Neuroscience, with particular interest in large-scale learning and inference over graphs. His past industrial research affiliations include HP Labs, Google Research, and Microsoft Research.
Inhaltsangabe
Introduction.- Graph Construction.- Learning and Inference.- Scalability.- Applications.- Future Work.- Bibliography.- Authors' Biographies.- Index .
