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

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.

為了使機器智能應用成功運作，機器必須在數據變化的情況下可靠地執行並能夠跟上數據流。《Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds》揭示了解決性能和可擴展性問題以實現更高可靠性的計算模型。它探索了使用機器智能實現模式識別的不同方法。

該書基於作者過去10年的研究，借鑒了模式識別、並行處理、分佈式系統和數據網絡的概念。它描述了關於模式識別的可擴展性和性能的基礎研究，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。作者回顧了許多方法並介紹了可能的可擴展性解決方案。

通過提供可靠且可擴展的模式識別所需的簡明知識，本書縮短了學習曲線並為您提供了寶貴的見解，以進一步創新。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，並提供了關於大型設備網絡編程的指導。