The dissimilarity representation for pattern recognition : foundations and applications / Elzbieta Pekalska, Robert P.W. Duin.

Format:

Book

Author/Creator:

Pękalska, Elżbieta.

Contributor:

Duin, Robert P. W.

Series:

Series in machine perception and artificial intelligence ; v. 64.

Series in machine perception and artificial intelligence ; v. 64

Language:

English

Subjects (All):

Pattern perception.

Pattern recognition systems.

Physical Description:

1 online resource (634 p.)

Edition:

1st ed.

Place of Publication:

New Jersey ; London : World Scientific, c2005.

Language Note:

English

Summary:

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 in

Contents:

Preface; Notation and basic terminology; Abbreviations; Contents; 1. Introduction; 1.1 Recognizing the pattern; 1.2 Dissimilarities for representation; 1.3 Learning from examples; 1.4 Motivation of the use of dissimilarity representations; 1.5 Relation to kernels; 1.6 Outline of the book; 1.7 In summary; PART 1 Concepts and theory; 2. Spaces; 3. Characterization of dissimilarities; 4. Learning approaches; 5. Dissimilarity measures; PART 2 Practice; 6. Visualization; 7. Further data exploration; 8. One-class classifiers; 9. Classification; 10. Combining

11. Representation review and recommendat ions12. Conclusions and open problems; Appendix A On convex and concave functions; Appendix B Linear algebra in vector spaces; B.l Some facts on matrices in a Euclidean space; B.2 Some facts on matrices in a pseudo-Euclidean space; Appendix C Measure and probability; Appendix D Statistical sidelines; D.l Likelihood and parameter estimation; D.2 Expectation-maximization (EM) algorithm; D.3 Model selection; D.4 PCA and probabilistic models; Appendix E Data sets; E.l Artificial data sets; E.2 Real-world data sets; Bibliography; Index

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9786611372873

9781281372871

1281372870

9789812703170

9812703179

OCLC:

476063399