Experiments with a featureless approach to pattern recognition
Pattern Recognition Letters - special issue on pattern recognition in practice V
Representation and recognition in vision
Representation and recognition in vision
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Dissimilarity-based classification for vectorial representations
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Geometric characterization and clustering of graphs using heat kernel embeddings
Image and Vision Computing
Beyond Traditional Kernels: Classification in Two Dissimilarity-Based Representation Spaces
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The dissimilarity space: Bridging structural and statistical pattern recognition
Pattern Recognition Letters
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Semi-supervised linear discriminant analysis through moment-constraint parameter estimation
Pattern Recognition Letters
Towards UCI+: A mindful repository design
Information Sciences: an International Journal
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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.