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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2 of 2 people found the following review helpful:
5.0 out of 5 stars
Smashing book on pattern recognition via dissimilarities,
By gilgamash (cologne, Germany) - See all my reviews
This review is from: The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence) (Hardcover)
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.
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Inside This Book
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First Sentence:
We recognize many patterns while observing the world. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
dissimilarity representations, dissimilarity space, general dissimilarity measures, traditional inner product, effective intrinsic dimension, product combiner, premetric space, prototype selection methods, additional validation set, dissimilarity data, generalized metric spaces, quasimetric space, raw stress, generalized closure operators, dissimilarity information, different dissimilarity measures, ionosphere data, irregular heptagons, mean combiner, square dissimilarities, fixed combiners, digit contours, trained combiners, pretopological space, approximate embedding New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
We recognize many patterns while observing the world. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
dissimilarity representations, dissimilarity space, general dissimilarity measures, traditional inner product, effective intrinsic dimension, product combiner, premetric space, prototype selection methods, additional validation set, dissimilarity data, generalized metric spaces, quasimetric space, raw stress, generalized closure operators, dissimilarity information, different dissimilarity measures, ionosphere data, irregular heptagons, mean combiner, square dissimilarities, fixed combiners, digit contours, trained combiners, pretopological space, approximate embedding New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 81
books:
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cites this book:
- Mathematical Taxonomy (Probability & Mathematical Statistics) by Nicholas Jardine in Back Matter
- String Searching Algorithms (Lecture Notes Series on Computing) by Graham A Stephen in Back Matter
- A Course in Functional Analysis by John B. Conway in Back Matter
- Web Document Analysis: Challenges and Opportunities by Apostolos Antonacopoulos in Front Matter
- Fighting Terror in Cyberspace (Series in Machine Perception & Artifical Intelligence) (Series in Machine Perception and Artifical Intelligence) by Mark Last in Front Matter
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