Item description for The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence) by Elzbieta Pekalska & Robert P. W. Duin...
This book provides a fundamentally new approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This 'dissimilarity representation' bridges the gap between the traditionally opposing approaches of statistical and structural pattern recognition. Physical phenomena, objects and events in the world are related in various and often complex ways. Such relations are usually modeled in the form of graphs or diagrams. While this is useful for communication between experts, such representation is difficult to combine and integrate by machine learning procedures. However, if the relations are captured by sets of dissimilarities, general data analysis procedures may be applied for analysis. With their detailed description of an unprecedented approach absent from traditional textbooks, the authors have crafted an essential book for every researcher and systems designer studying or developing pattern recognition systems.
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Studio: World Scientific Publishing Company
Est. Packaging Dimensions: Length: 9.13" Width: 6.38" Height: 1.5" Weight: 2.29 lbs.
Release Date Dec 23, 2005
Publisher World Scientific Publishing Company
ISBN 9812565302 ISBN13 9789812565303
Reviews - What do customers think about The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)?
Smashing book on pattern recognition via dissimilarities Sep 11, 2007
Being the very first book to present the current state of the art when it comes to dissimilarity representation for pattern recognition (in the field of numerical data representation), it is outstanding as it introduces both, the mathematical background like the spaces one is working in, the techniques like embedding dissimilarities into (pseudo)-euclidean spaces and how all this combines. Next, this knowledge is used to describe the actions of learning, visualizing and creating classes and connecting new feature vectors of new data to their classes (as far as possible). The algorithmical and implementational aspects are not really dealt with, which one could judge being a disadvantage; in my opinion however, this is not the intention of the book.