Introduction to Lattice Algebra: With Applications in Ai, Pattern Recognition, Image Analysis, and Biomimetic Neural Networks
暫譯: 格子代數入門：在人工智慧、模式識別、影像分析及仿生神經網絡中的應用

Ritter, Gerhard X., Urcid, Gonzalo

Introduction to Lattice Algebra: With Applications in Ai, Pattern Recognition, Image Analysis, and Biomimetic Neural Networks
  • 出版商: CRC
  • 出版日期: 2021-08-24
  • 售價: $4,910
  • 貴賓價: 9.5 折 $4,665
  • 語言: 英文
  • 頁數: 418
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 0367720299
  • ISBN-13: 9780367720292

    • 海外代購書籍(需單獨結帳)

相關主題

商品描述

Lattice theory extends into virtually every branch of mathematics, ranging from measure theory and convex geometry to probability theory and topology. A more recent development has been the rapid escalation of employing lattice theory for various applications outside the domain of pure mathematics. These applications range from electronic communication theory and gate array devices that implement Boolean logic to artificial intelligence and computer science in general.

Introduction to Lattice Theory: With Applications in AI, Pattern Recognition, Image Analysis, and Biomimetic Neural Networks lays emphasis on two subjects, the first being lattice algebra and the second the practical applications of that algebra. This textbook is intended to be used for a special topics course in artificial intelligence with focus on pattern recognition, multispectral image analysis, and biomimetic artificial neural networks. The book is self-contained and - depending on the student's major - can be used at a senior undergraduate level or a first-year graduate level course. The book is also an ideal self-study guide for researchers and professionals in the above-mentioned disciplines.

Features

  • Filled with instructive examples and exercises to help build understanding
  • Suitable for researchers, professionals and students, both in mathematics and computer science
  • Every chapter consists of exercises with solution provided online at www.Routledge.com/9780367720292

商品描述(中文翻譯)

格子理論幾乎延伸到數學的每一個分支，從測度理論和凸幾何到概率論和拓撲學。最近的一個發展是格子理論在純數學領域之外的各種應用迅速增加。這些應用範圍從電子通信理論和實現布爾邏輯的閘陣列設備到人工智慧和計算機科學等領域。

《格子理論導論：在人工智慧、模式識別、影像分析和仿生神經網絡中的應用》強調了兩個主題，第一個是格子代數，第二個是該代數的實際應用。本教科書旨在用於人工智慧的專題課程，重點在於模式識別、多光譜影像分析和仿生人工神經網絡。這本書是自成一體的，根據學生的主修，可以用於大四本科生或一年級研究生的課程。這本書也是上述學科研究人員和專業人士的理想自學指南。

特色
- 充滿啟發性的範例和練習，幫助建立理解
- 適合數學和計算機科學領域的研究人員、專業人士和學生
- 每一章都包含練習，解答可在線獲得，網址為 www.Routledge.com/9780367720292

作者簡介

Gerhard X. Ritter received both his B.A. degree with honors in 1966 and his Ph.D. degree in Mathematics in 1971 from the University of Wisconsin-Madison. He is a Florida Blue Key Distinguished Professor Emeritus in both the Department of Mathematics and the Department of Computer and Information Science and Engineering (CISE) of the University of Florida. He was the Chair of the CISE department from 1994 to 2001, and Acting Chair from 2011 to 2012.

Professor Ritter has written more than 140 research papers in subjects ranging from pure and applied mathematics to pattern recognition, computer vision, and artificial neural networks. He is the founding editor of the Journal of Mathematical Imaging and Vision, and founding member and first chair of the Society for Industrial and Applied Mathematics (SIAM) Activity Group on Imaging Science (SIAG-IS). He was a member of the Deputy Undersecretary of Defense for Research and Advanced Technology's advanced technology research on emerging technologies panel (1988) and a member of the advanced sensors committee on key technologies for the 1990s, formed by the same undersecretary (1989). Among other U.S. government-requested briefings attended by Professor Ritter were the annual Automatic Target Recognition Working Group (ATRWG) meetings held across the U.S. (1984-1996 and 2003). For his contribution, he was awarded the General Ronald W. Yates Award for Excellence in Technology Transfer by the U.S. Air Force Research Laboratory (1998). Among honors outside the realm of the Department of Defense are the Silver Core Award of the International Federation for Information Processing (1989); the Best Session Award at the American Society for Engineering Education (ASEE) Conference for Industry and Education Collaboration in San Jose, CA (1996); and the Best Paper Presentation Award at the International Joint Conference on Neural Networks (IJCNN) sponsored by the Institute of Electrical and Electronics Engineers Neural Networks Council (IEEE/NNC) and the International Neural Network Society (INNS) in Washington, DC (1999).

Gonzalo Urcid received his Bachelor degree in Communications and Electronic Engineering (1982) and his Master degree in Computational and Information Systems (1985) both from the University of the Americas in Puebla (UDLAP), Mexico. He has a Ph.D. degree (1999) in Optical Sciences from the National Institute of Astrophysics, Optics, and Electronics (INAOE) in Tonantzintla, Mexico and made a postdoctoral residence, between 2001 and 2002, as invited faculty at the CISE Department, University of Florida. Also, from 2001 to 2020 was awarded the distinction of National Researcher from the Mexican National Council of Science and Technology (SNI-CONACYT). Currently is an Associate Professor in the Optics Department at INAOE. His research interests include digital image processing and analysis, artificial neural networks based on lattice algebra, and lattice computing applied to artificial intelligence and pattern recognition.

作者簡介(中文翻譯)

Gerhard X. Ritter 於1966年獲得威斯康辛大學麥迪遜分校的榮譽學士學位，並於1971年獲得數學博士學位。他是佛羅里達大學數學系及計算機與資訊科學與工程系（CISE）的佛羅里達藍鑰傑出名譽教授。他於1994年至2001年間擔任CISE系主任，並於2011年至2012年間擔任代理系主任。

Ritter教授在純數學和應用數學、模式識別、計算機視覺及人工神經網絡等領域撰寫了超過140篇研究論文。他是Journal of Mathematical Imaging and Vision的創始編輯，也是工業與應用數學學會（SIAM）成像科學活動小組（SIAG-IS）的創始成員及首任主席。他曾是國防部研究與先進技術副部長的新興技術高科技研究小組成員（1988年），以及同一副部長成立的1990年代關鍵技術高級感測器委員會的成員（1989年）。Ritter教授參加的其他美國政府要求的簡報會包括1984年至1996年及2003年在美國各地舉行的年度自動目標識別工作小組（ATRWG）會議。因其貢獻，他於1998年獲得美國空軍研究實驗室頒發的羅納德·W·耶茲將軍技術轉移卓越獎。除了國防部的榮譽外，他還獲得了國際資訊處理聯合會的銀核心獎（1989年）；在1996年於加州聖荷西舉行的美國工程教育學會（ASEE）產業與教育合作會議上獲得最佳會議獎；以及在1999年於華盛頓特區舉行的由電氣與電子工程師學會神經網絡委員會（IEEE/NNC）和國際神經網絡學會（INNS）主辦的國際聯合神經網絡會議（IJCNN）上獲得最佳論文報告獎。

Gonzalo Urcid 於1982年獲得墨西哥普埃布拉美洲大學（UDLAP）通信與電子工程學士學位，並於1985年獲得計算與資訊系統碩士學位。他於1999年在墨西哥托南辛特拉的國家天文學、光學與電子學研究所（INAOE）獲得光學科學博士學位，並於2001年至2002年間作為受邀教員在佛羅里達大學CISE系進行博士後研究。此外，他於2001年至2020年間獲得墨西哥國家科學與技術委員會（SNI-CONACYT）頒發的國家研究員榮譽。目前，他是INAOE光學系的副教授。他的研究興趣包括數位影像處理與分析、基於格代數的人工神經網絡，以及應用於人工智慧和模式識別的格計算。

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