Machine Learning and Pattern Recognition

PUBLISHED


Viewing: 1 - 10 of 85
Published:
February 18, 2014
Author(s):
Anthony Louis Almudevar
Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing tools for the derivation of error bounds and 
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Published:
December 20, 2013
Author(s):
Pierre Bessiere, Emmanuel Mazer, Juan Manuel Ahuactzin, Kamel Mekhnacha
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to 
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Published:
December 11, 2013
Author(s):
Haiping Lu, Konstantinos N. Plataniotis, Anastasios Venetsanopoulos
Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient 
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Published:
December 09, 2013
Editor(s):
Dakshina Ranjan Kisku, Phalguni Gupta, Jamuna Kanta Sing
Although biometric systems present powerful alternatives to traditional authentication schemes, there are still many concerns about their security. Advances in Biometrics for Secure Human Authentication and Recognition showcases some of the latest technologies and algorithms being used for human 
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Published:
December 05, 2013
Author(s):
Miguel A. Labrador, Oscar D. Lara Yejas
Learn How to Design and Implement HAR Systems The pervasiveness and range of capabilities of today’s mobile devices have enabled a wide spectrum of mobile applications that are transforming our daily lives, from smartphones equipped with GPS to integrated mobile sensors that acquire physiological 
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$99.95
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Published:
November 04, 2013
Author(s):
Liang Sun, Shuiwang Ji, Jieping Ye
Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data 
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Published:
October 25, 2013
Editor(s):
Markus Hofmann, Ralf Klinkenberg
Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in today’s world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making 
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Published:
September 11, 2013
Author(s):
Vlad Atanasiu
Expert Bytes: Computer Expertise in Forensic Documents — Players, Needs, Resources and Pitfalls —introduces computer scientists and forensic document examiners to the computer expertise of forensic documents and assists them with the design of research projects in this interdisciplinary field. 
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$79.95
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Published:
August 21, 2013
Editor(s):
Charu C. Aggarwal, Chandan K. Reddy
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, 
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Published:
July 15, 2013
Editor(s):
Nagiza F. Samatova, William Hendrix, John Jenkins, Kanchana Padmanabhan, Arpan Chakraborty
Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring 
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Data Mining "Nobel Prize" Awarded to Series Editor Vipin Kumar

The ACM SIGKDD 2012 Innovation Award, the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), has been awarded to Prof. Vipin Kumar for his technical contributions to foundational research in data mining and its applications to mining scientific and climate data. Prof. Kumar is the editor of the Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. He has pioneered research in the areas of high performance and parallel data mining. His research group has also been at the forefront in the development of data-driven discovery methods for analyzing global climate and ecosystem data.

 

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