This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighour based classifiers, current topics - neural networks, support vector machines and decision trees - attributed to the recent vast progress in this field are also dealt with. Pattern Recognition: An Algorithmic Approach will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter.
This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systemat...
As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and inefficient computation times.
This unique text/reference addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better...
As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and ine...
This book constitutes the refereed conference proceedings of the 8th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2014, held in Bangalore, India, in December 2014. The 22 revised full papers were carefully reviewed and selected from 44 submissions. The papers feature a wide range of topics covering both theory, methods and tools as well as their diverse applications in numerous domains.
This book constitutes the refereed conference proceedings of the 8th International Conference on Multi-disciplinary Trends in Artificial Intelligence,...
As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and inefficient computation times.
This unique text/reference addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better...
As data mining algorithms are typically applied to sizable volumes of high-dimensional data, these can result in large storage requirements and ine...
Presents the latest scholarly research on handling large data sets with conventional data mining and provides information about the new technologies developed for the management of large data. Featuring coverage on a broad range of topics such as text and web data analytics, risk analysis, and opinion mining, this publication is designed for professionals, researchers, and students.
Presents the latest scholarly research on handling large data sets with conventional data mining and provides information about the new technologies d...
