Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Representation and Recognition of Handwritten Digits Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Localized content based image retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Graph Classification Based on Dissimilarity Space Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Support vector machine classifiers for asymmetric proximities
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Graph embedding in vector spaces by means of prototype selection
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Non-Euclidean dissimilarities: causes and informativeness
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Bag dissimilarities for multiple instance learning
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
The dissimilarity space: Bridging structural and statistical pattern recognition
Pattern Recognition Letters
Beyond Traditional Kernels: Classification in Two Dissimilarity-Based Representation Spaces
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Class-dependent dissimilarity measures for multiple instance learning
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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A widely used approach to cope with asymmetry in dissimilarities is by symmetrizing them. Usually, asymmetry is corrected by applying combiners such as average, minimum or maximum of the two directed dissimilarities. Whether or not these are the best approaches for combining the asymmetry remains an open issue. In this paper we study the performance of the extended asymmetric dissimilarity space (EADS) as an alternative to represent asymmetric dissimilarities for classification purposes. We show that EADS outperforms the representations found from the two directed dissimilarities as well as those created by the combiners under consideration in several cases. This holds specially for small numbers of prototypes; however, for large numbers of prototypes the EADS may suffer more from overfitting than the other approaches. Prototype selection is recommended to overcome overfitting in these cases.