Combining Pattern Classifiers, 2nd Edition: Methods and Algorithms

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Share This Paper. Figures, Tables, and Topics from this paper. Figures and Tables. Citations Publications citing this paper. Performance and diversity evaluation in hybrid and non-hybrid structures of ensembles Anne M. Santos , Marjory C.

Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data

Enhanced ensemble-based object detection in medical image analysis Laszlo Kovacs. Coupling different methods for overcoming the class imbalance problem Loris Nanni , Carlo Fantozzi , Nicola Lazzarini. Chemical speciation on urinary lithiasis. Image analysis and separation techniques for the study of lithogenesis F Lucena.

Hierarchical information representation and efficient classification of gene expression microarray data Mattia Bosio. ISBN: Hardcover pages August Description This book is about the use of modern statistical methods for quality control and improvement. It provides comprehensive coverage of the subject from basic principles to state-of-art concepts and applications.

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Combining Pattern Classifiers: Methods and Algorithms

By presenting theory, and supporting the theory with clear and relevant examples, Montgomery helps the reader to understand the big picture of important concepts. Updated to reflect contemporary practice and provide more information on management aspects of quality improvement. ISBN: Paperback pages September Description The Concise Encyclopedia of Computer Science is the perfect desk reference to guide you beyond the hype and jargon that entangle computer technology and its ubiquitous applications.

With many contributions by acknowledged experts, responsible for the key developments that have shaped the discipline, articles are classified within nine main themes: Hardware Computer Systems Information and Data Software Mathematics of Computing Theory of Computation Methodologies Applications Computing Milieux This structure enables you to follow a coherent pattern of study or research in any given subfield. Extensive cross-referencing at the start of every article provides a clear roadmap to lead you to key related topics.

In short, all the back-up you'll need whether you are studying, teaching or working in the field of computing.


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Combining Pattern Classifiers Methods and Algorithms 2nd Edition

The second part of the book covers advanced topics, such as multivariate and Bayesian modelling of extremes. Provides comprehensive coverage of a growing area of research. Provides a good balance of theory and real-world applications. Includes coverage of time series, regression, multivariate and Bayesian modelling. Introduces a number of case studies early in the book and develops the theory around them. Illustrated with many data examples and plots.