9781466510968 - internet-scale pattern recognition: new techniques for voluminous data sets and data clouds di muhamad amin, anang; khan, asad; nasution, benny (12 risultati)

Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condizione: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

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Hardcover. Condizione: new. 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. This cutting-edge reference outlines the underlying theory and principles of efficient and effective distributed pattern recognition involving one-shot learning and in-network processing for different types of applications, including multimedia retrieval systems and event detection over different network environments. Investigating one-shot learning and in-network processing as complementary mechanisms for efficient and accurate distributed pattern analyses, it presents the technical aspects related to the development of scalable pattern recognition using a number of contemporary application development tools. It also considers scalability of pattern recognition schemes when dealing with such data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: As New. Unread book in perfect condition.

Condizione: New. Num Pages: 197 pages, 76 black & white illustrations, 15 black & white tables. BIC Classification: UNF; UYQP. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 186 x 239 x 15. Weight in Grams: 468. . 2012. Hardback. . . . .

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. 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 mast.

Hardcover. Condizione: Brand New. 300 pages. 9.29x6.42x0.63 inches. In Stock.

Condizione: New. Num Pages: 197 pages, 76 black & white illustrations, 15 black & white tables. BIC Classification: UNF; UYQP. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 186 x 239 x 15. Weight in Grams: 468. . 2012. Hardback. . . . . Books ship from the US and Ireland.

Hardcover. Condizione: new. 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. This cutting-edge reference outlines the underlying theory and principles of efficient and effective distributed pattern recognition involving one-shot learning and in-network processing for different types of applications, including multimedia retrieval systems and event detection over different network environments. Investigating one-shot learning and in-network processing as complementary mechanisms for efficient and accurate distributed pattern analyses, it presents the technical aspects related to the development of scalable pattern recognition using a number of contemporary application development tools. It also considers scalability of pattern recognition schemes when dealing with such data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…