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Machine Learning Algorithms: Adversarial Robustness in Signal Processing by Fuwe

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Specificaties

Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
ISBN-13
9783031163746
Book Title
Machine Learning Algorithms
ISBN
9783031163746
Publication Year
2022
Series
Wireless Networks Ser.
Type
Textbook
Format
Hardcover
Language
English
Publication Name
Machine Learning Algorithms : Adversarial Robustness in Signal Processing
Author
Shuguang Cui, Lifeng Lai, Fuwei Li
Item Length
9.3in
Publisher
Springer International Publishing A&G
Item Width
6.1in
Item Weight
12.3 Oz
Number of Pages
IX, 104 Pages

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Product Information

This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal processing algorithms against adversarial attacks. Since data quality is crucial in signal processing, the adversary that can poison the data will be a significant threat to signal processing. Therefore, it is necessary and urgent to investigate the behavior of machine learning algorithms in signal processing under adversarial attacks. The authors in this book mainly examine the adversarial robustness of three commonly used machine learning algorithms in signal processing respectively: linear regression, LASSO-based feature selection, and principal component analysis (PCA). As to linear regression, the authors derive the optimal poisoning data sample and the optimal feature modifications, and also demonstrate the effectiveness of the attack against a wireless distributed learning system. The authors further extend the linear regression to LASSO-based feature selection and study the best strategy to mislead the learning system to select the wrong features. The authors find the optimal attack strategy by solving a bi-level optimization problem and also illustrate how this attack influences array signal processing and weather data analysis. In the end, the authors consider the adversarial robustness of the subspace learning problem. The authors examine the optimal modification strategy under the energy constraints to delude the PCA-based subspace learning algorithm. This book targets researchers working in machine learning, electronic information, and information theory as well as advanced-level students studying these subjects. R&D engineers who are working in machine learning, adversarial machine learning, robust machine learning, and technical consultants working on the security and robustness of machine learning are likely to purchase this book as a reference guide.

Product Identifiers

Publisher
Springer International Publishing A&G
ISBN-10
3031163745
ISBN-13
9783031163746
eBay Product ID (ePID)
17057284862

Product Key Features

Author
Shuguang Cui, Lifeng Lai, Fuwei Li
Publication Name
Machine Learning Algorithms : Adversarial Robustness in Signal Processing
Format
Hardcover
Language
English
Publication Year
2022
Series
Wireless Networks Ser.
Type
Textbook
Number of Pages
IX, 104 Pages

Dimensions

Item Length
9.3in
Item Width
6.1in
Item Weight
12.3 Oz

Additional Product Features

Number of Volumes
1 Vol.
Lc Classification Number
Q325.5-.7
Table of Content
Chapter. 1. Introduction.- Chapter. 2. Optimal Feature Manipulation Attacks Against Linear Regression.- Chapter. 3. On the Adversarial Robustness of LASSO Based Feature Selection.- Chapter. 4. On the Adversarial Robustness of Subspace Learning.- Chapter. 5. Summary and Extensions.- Chapter. 6. Appendix.
Copyright Date
2022
Topic
Mobile & Wireless Communications, Probability & Statistics / General, Intelligence (Ai) & Semantics, General
Dewey Decimal
006.31
Dewey Edition
23
Illustrated
Yes
Genre
Computers, Technology & Engineering, Science, Mathematics

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Lovely styled images of bohemian fairies. Only issue is with pixels show on edges of images. Will need to trace onto other paper but have never run into this problem before. More care of printed images would make this the best artwork ordered.