Mean Field Guided Machine Learning
close menu
Bookswagon
search
My Account
Home > Computer Science Books > Artificial intelligence > Machine learning > Mean Field Guided Machine Learning: (Wireless Networks)
Mean Field Guided Machine Learning: (Wireless Networks)

Mean Field Guided Machine Learning: (Wireless Networks)


     0     
5
4
3
2
1



Out of Stock


Notify me when this book is in stock
X
About the Book

This book explores the integration of Mean Field Game (MFG) theory with machine learning (ML), presenting both theoretical foundations and practical applications.  Drawing from extensive research, it provides insights into how MFG can improve various ML techniques, including supervised learning, reinforcement learning, and federated learning.

MFG theory and ML are converging to address critical challenges in high-dimensional spaces and multi-agent systems. While ML has transformed industries by leveraging vast data and computational power, scalability and robustness remain key concerns. MFG theory, which models large populations of interacting agents, offers a mathematical framework to simplify and optimize complex systems, enhancing ML’s efficiency and applicability.

By bridging these two fields, this book aims to drive innovation in scalable and robust machine learning. The integration of MFG with ML not only expands research possibilities but also paves the way for more adaptive and intelligent systems. Through this work, the authors hope to inspire further exploration and development in this promising interdisciplinary domain. With case studies and real-world examples, this book serves as a guide for researchers and students in communications and networks seeking to harness MFG’s potential in advancing ML.  Industry managers, practitioners and government research workers in the fields of communications and networks will find this book a valuable resource as well.



About the Author :
Dr, Yuhan Kang received the B.S. degree from the School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China, in 2019, and the Ph.D. degree with the Electrical and Computer Engineering Department, the University of Houston, Houston, TX, USA in 2023. Currently he is AI Research Scientist for Weichai America Corp. His research interest include mean-field game theory, machine learning, deep learning, Internet-of-Things networks, and optimization theory. Dr. Hao Gao received the B.E. degree in electrical and information engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2018, and Ph.D. degree in electrical engineering with the University of Houston, Houston, TX, USA in 2022. Currently he is senior Engineering in Samsung, California, USA. His research interests include mean field game, machine learning, and related applications in wireless communication. Dr. Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. Currently, he is a John and Rebecca Moores Professor in the Electrical and Computer Engineering Department as well as in the Computer Science Department at the University of Houston, Texas. Dr. Han received IEEE fellow since 2014, AAAS fellow since 2019, and ACM Fellow since 2024. Dr. Han is a 1% highly cited researcher since 2017 according to Web of Science. Dr. Han is also the winner of the 2021 IEEE Kiyo Tomiyasu Award (an IEEE Field Award), for outstanding early to mid-career contributions to technologies holding the promise of innovative applications, with the following citation: for contributions to game theory and distributed management of autonomous communication networks.


Best Sellers


Product Details
  • ISBN-13: 9783031918612
  • Publisher: Springer International Publishing AG
  • Publisher Imprint: Springer International Publishing AG
  • Height: 235 mm
  • No of Pages: 150
  • Returnable: N
  • Returnable: N
  • Returnable: N
  • Returnable: N
  • Series Title: Wireless Networks
  • ISBN-10: 3031918614
  • Publisher Date: 09 Jul 2026
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Returnable: N
  • Returnable: N
  • Returnable: N
  • Returnable: N
  • Width: 155 mm


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Mean Field Guided Machine Learning: (Wireless Networks)
Springer International Publishing AG -
Mean Field Guided Machine Learning: (Wireless Networks)
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Mean Field Guided Machine Learning: (Wireless Networks)

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    Fresh on the Shelf


    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!
    Your IP: 216.73.216.226 IN