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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Information Theoretic Learning-Based Filter: Algorithms, Analysis and Applications

Information Theoretic Learning-Based Filter: Algorithms, Analysis and Applications

Zhao, Haiquan
Hou, Xinyan
Long, Xiaoqiang

Hardcover

Technology & EngineeringGeneral Computers

PREORDER - Expected ship date November 23, 2026

ISBN10: 3032296226
ISBN13: 9783032296221
Publisher: Springer
Published: Nov 23 2026
Pages: 202
Language: English

This book provides a comprehensive and in-depth exploration of adaptive filtering algorithms based on the Information Theoretic Learning (ITL). As a powerful alternative to traditional second-order statistical methods, ITL-based adaptive filtering algorithms are particularly effective in dealing with non-Gaussian noise. The book systematically introduces core ITL criteria such as minimum error entropy and maximum correntropy and extends these principles to the field of multidimensional signal processing and nonlinear adaptive filtering, demonstrating their effectiveness through modeling real-world signals like wind speed and temperature. In addition to single-node filtering, this book thoroughly investigates distributed adaptive filtering, addressing collaborative learning across networked systems. It further integrates graph signal processing, allowing for efficient modeling and analysis of signals defined on irregular or structured domains. Together, these contributions showcase ITL as a unified and powerful learning framework, advancing adaptive filtering theory and methodology across linear, nonlinear, distributed, and graph-based signal processing environments.

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