Representation and Recognition of Handwritten Digits Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Dissimilarity representations allow for building good classifiers
Pattern Recognition Letters
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
On a theory of learning with similarity functions
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
A fuzzy image metric with application to fractal coding
IEEE Transactions on Image Processing
A theory of learning with similarity functions
Machine Learning
Supervised Isomap with Dissimilarity Measures in Embedding Learning
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A Max-Margin Learning Algorithm with Additional Features
FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
A Large Margin Classifier with Additional Features
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
The Journal of Machine Learning Research
Learning good edit similarities with generalization guarantees
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
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We study the problem of learning a classification task in which only a dissimilarity function of the objects is accessible. That is, data are not represented by feature vectors but in terms of their pairwise dissimilarities. We investigate the sufficient conditions for dissimilarity functions to allow building accurate classifiers. Our results have the advantages that they apply to unbounded dissimilarities and are invariant to order-preserving transformations. The theory immediately suggests a learning paradigm: construct an ensemble of decision stumps each depends on a pair of examples, then find a convex combination of them to achieve a large margin. We next develop a practical algorithm called Dissimilarity based Boosting (DBoost) for learning with dissimilarity functions under the theoretical guidance. Experimental results demonstrate that DBoost compares favorably with several existing approaches on a variety of databases and under different conditions.