Feature-Based Dissimilarity Space Classification
General dissimilarity-based learning approaches have been proposed for dissimilarity data sets [1,2]. They often arise in problems in which direct comparisons of objects are made by computing pairwise distances between images, spectra, graphs or strings. Dissimilarity-based classifiers can also be d...
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| Published in | Recognizing Patterns in Signals, Speech, Images and Videos pp. 46 - 55 |
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| Main Authors | , , , |
| Format | Book Chapter |
| Language | English |
| Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2010
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| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783642177101 3642177107 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-642-17711-8_5 |
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| Abstract | General dissimilarity-based learning approaches have been proposed for dissimilarity data sets [1,2]. They often arise in problems in which direct comparisons of objects are made by computing pairwise distances between images, spectra, graphs or strings.
Dissimilarity-based classifiers can also be defined in vector spaces [3]. A large comparative study has not been undertaken so far. This paper compares dissimilarity-based classifiers with traditional feature-based classifiers, including linear and nonlinear SVMs, in the context of the ICPR 2010 Classifier Domains of Competence contest. It is concluded that the feature-based dissimilarity space classification performs similar or better than the linear and nonlinear SVMs, as averaged over all 301 datasets of the contest and in a large subset of its datasets. This indicates that these classifiers have their own domain of competence. |
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| AbstractList | General dissimilarity-based learning approaches have been proposed for dissimilarity data sets [1,2]. They often arise in problems in which direct comparisons of objects are made by computing pairwise distances between images, spectra, graphs or strings.
Dissimilarity-based classifiers can also be defined in vector spaces [3]. A large comparative study has not been undertaken so far. This paper compares dissimilarity-based classifiers with traditional feature-based classifiers, including linear and nonlinear SVMs, in the context of the ICPR 2010 Classifier Domains of Competence contest. It is concluded that the feature-based dissimilarity space classification performs similar or better than the linear and nonlinear SVMs, as averaged over all 301 datasets of the contest and in a large subset of its datasets. This indicates that these classifiers have their own domain of competence. |
| Author | Loog, Marco Pȩkalska, Elżbieta Duin, Robert P. W. Tax, David M. J. |
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| Copyright | Springer-Verlag Berlin Heidelberg 2010 |
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| DOI | 10.1007/978-3-642-17711-8_5 |
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| Editor | Ünay, Devrim Çataltepe, Zehra Aksoy, Selim |
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| PublicationSubtitle | ICPR 2010 Contests, Istanbul, Turkey, August 23-26, 2010, Contest Reports |
| PublicationTitle | Recognizing Patterns in Signals, Speech, Images and Videos |
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| SubjectTerms | Dissimilarity Measure Feature Space Linear Support Vector Machine Neighbor Rule Training Object |
| Title | Feature-Based Dissimilarity Space Classification |
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