http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Secure Networked Inference with Unreliable Data Sources
XIII, 208 p. 74 illus., 71 illus. in color. online resource.
The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.
Chapter 1 Introduction -- Chapter 2 Conventional Inference theories -- Chapter 3 Distributed Detection in Networks -- Chapter 4 Distributed Estimation and Target Localization -- Chapter 5 Distributed Classification and Target Tracking -- Chapter 6 New Research Directions Discussion and conclusions.
by Aditya Vempaty, Bhavya Kailkhura, Pramod K. Varshney.
Probability and Statistics in Computer Science