Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds
暫譯: 網際網路規模的模式識別：針對大量數據集和數據雲的新技術

Muhamad Amin, Anang, Khan, Asad, Nasution, Benny

Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds

商品描述

For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational models that address performance and scalability to achieve higher levels of reliability. It explores different ways of implementing pattern recognition using machine intelligence.





Based on the authors' research from the past 10 years, the text draws on concepts from pattern recognition, parallel processing, distributed systems, and data networks. It describes fundamental research on the scalability and performance of pattern recognition, addressing issues with existing pattern recognition schemes for Internet-scale data deployment. The authors review numerous approaches and introduce possible solutions to the scalability problem.





By presenting the concise body of knowledge required for reliable and scalable pattern recognition, this book shortens the learning curve and gives you valuable insight to make further innovations. It offers an extendable template for Internet-scale pattern recognition applications as well as guidance on the programming of large networks of devices.

商品描述(中文翻譯)

為了讓機器智慧應用成功運作，機器必須在數據變化下可靠地執行，並且能夠跟上數據流。《Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds》揭示了針對性能和可擴展性所設計的計算模型，以實現更高的可靠性。它探討了使用機器智慧實現模式識別的不同方法。

基於作者過去十年的研究，該文本借鑒了模式識別、並行處理、分散式系統和數據網絡的概念。它描述了有關模式識別的可擴展性和性能的基本研究，解決了現有模式識別方案在互聯網規模數據部署中的問題。作者回顧了多種方法並介紹了可行的可擴展性問題解決方案。

通過呈現可靠且可擴展的模式識別所需的簡明知識體系，本書縮短了學習曲線，並為您提供了寶貴的見解，以便進一步創新。它提供了一個可擴展的模板，用於互聯網規模的模式識別應用，以及對大型設備網絡編程的指導。

作者簡介

Anang Hudaya Muhamad Amin is a senior lecturer in the Faculty of Information Science and Technology at Multimedia University in Malaysia. He received a BTech (Hons.) in information technology from Universiti Teknologi PETRONAS and a masters in network computing and PhD from Monash University. His research interests include artificial intelligence with specialization in distributed pattern recognition and bio-inspired computational intelligence, wireless sensor networks, and distributed computing.



Asad I. Khan is a senior lecturer in the Faculty of Information Technology at Monash University. Dr. Khan is an Australian Research Council assessor and has published over 80 refereed papers. His research areas include parallel computation, neural networks, and distributed pattern recognition as well as the development of e-research systems and intelligent sensor networks.



Benny Nasution is with the Department of Computer Engineering at Politeknik Negeri Medan. Dr. Nasution was awarded the IBM Award from Tokyo Research Lab and the Mollie Holman Medal from Monash University.

作者簡介(中文翻譯)

Anang Hudaya Muhamad Amin 是馬來西亞多媒體大學資訊科學與技術學院的資深講師。他在國立石油科技大學（Universiti Teknologi PETRONAS）獲得資訊科技的榮譽學士學位（BTech (Hons.)），並在莫納什大學（Monash University）獲得網路計算碩士學位及博士學位。他的研究興趣包括人工智慧，專注於分散式模式識別和生物啟發的計算智慧、無線感測器網路以及分散式計算。



Asad I. Khan 是莫納什大學資訊科技學院的資深講師。Khan 博士是澳大利亞研究委員會的評估員，並已發表超過80篇經過審核的論文。他的研究領域包括平行計算、神經網路和分散式模式識別，以及電子研究系統和智能感測器網路的開發。



Benny Nasution 目前在美丹國立工藝學院（Politeknik Negeri Medan）的計算機工程系任職。Nasution 博士曾獲得來自東京研究實驗室的 IBM 獎和來自莫納什大學的 Mollie Holman 獎章。

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