Information Theoretic Learning-based Filter

Name: Information Theoretic Learning-based Filter
Price: 433.75 AUD
Availability: PreOrder

Algorithms, Analysis and Applications

By: Haiquan Zhao, Xinyan Hou, Xiaoqiang Long

Write A Review

Hardcover | 23 November 2026

At a Glance

Format
Hardcover

Dimensions(cm)
23.5 x 15.5

Hardcover

$433.75

or 4 interest-free payments of $108.44 with

or

Available: 23rd November 2026

Preorder. Will ship when available.

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.

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $79.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.