Representation and recognition in vision
Representation and recognition in vision
Classification with Nonmetric Distances: Image Retrieval and Class Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Combining Multiple Representations and Classifiers for Pen-based Handwritten Digit Recognitio
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
On Combining Dissimilarity Representations
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning with non-positive kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature Space Interpretation of SVMs with Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for dyadic data
Neural Computation
Dissimilarity-based classification for vectorial representations
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
Invariant kernel functions for pattern analysis and machine learning
Machine Learning
Kernel Discriminant Analysis for Positive Definite and Indefinite Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Spherical embedding and classification
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Prototype Selection for Dissimilarity Representation by a Genetic Algorithm
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Feature-based dissimilarity space classification
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Transforming strings to vector spaces using prototype selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Beyond Traditional Kernels: Classification in Two Dissimilarity-Based Representation Spaces
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Bag dissimilarities for multiple instance learning
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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
One-sided prototype selection on class imbalanced dissimilarity matrices
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Further results on dissimilarity spaces for hyperspectral images RF-CBIR
Pattern Recognition Letters
On the informativeness of asymmetric dissimilarities
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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Human experts constitute pattern classes of natural objects based on their observed appearance. Automatic systems for pattern recognition may be designed on a structural description derived from sensor observations. Alternatively, training sets of examples can be used in statistical learning procedures. They are most powerful for vectorial object representations. Unfortunately, structural descriptions do not match well with vectorial representations. Consequently it is difficult to combine the structural and statistical approaches to pattern recognition. Structural descriptions may be used to compare objects. This leads to a set of pairwise dissimilarities from which vectors can be derived for the purpose of statistical learning. The resulting dissimilarity representation bridges thereby the structural and statistical approaches. The dissimilarity space is one of the possible spaces resulting from this representation. It is very general and easy to implement. This paper gives a historical review and discusses the properties of the dissimilarity space approaches illustrated by a set of examples on real world datasets.