Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Protein homology detection using string alignment kernels
Bioinformatics
On a theory of learning with similarity functions
ICML '06 Proceedings of the 23rd international conference on Machine learning
A theory of learning with similarity functions
Machine Learning
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
How good is a kernel when used as a similarity measure?
COLT'07 Proceedings of the 20th annual conference on Learning theory
A fuzzy image metric with application to fractal coding
IEEE Transactions on Image Processing
Linear Programming Boosting by Column and Row Generation
DS '09 Proceedings of the 12th International Conference on Discovery Science
Semantics-driven approach for automatic selection of best views of 3D shapes
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
International Journal of Data Mining and Bioinformatics
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We study the problem of classification when only a dissimilarity function between objects is accessible. That is, data samples are represented not by feature vectors but in terms of their pairwise dissimilarities. We establish sufficient conditions for dissimilarity functions to allow building accurate classifiers. The theory immediately suggests a learning paradigm: construct an ensemble of simple classifiers, each depending on a pair of examples; then find a convex combination of them to achieve a large margin. We next develop a practical algorithm referred to as dissimilarity-based boosting (DBoost) for learning with dissimilarity functions under theoretical guidance. Experiments on a variety of databases demonstrate that the DBoost algorithm is promising for several dissimilarity measures widely used in practice.