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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Duin, R.P.W., Loog, M., Pȩkalska, E., Tax, D.M.J. (2010). Feature-Based Dissimilarity Space Classification. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds) Recognizing Patterns in Signals, Speech, Images and Videos. ICPR 2010. Lecture Notes in Computer Science, vol 6388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17711-8_5
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DOI: https://doi.org/10.1007/978-3-642-17711-8_5
Publisher Name: Springer, Berlin, Heidelberg
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