Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Boosting margin based distance functions for clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Kernel methods for predicting protein--protein interactions
Bioinformatics
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Learning distance function by coding similarity
Proceedings of the 24th international conference on Machine learning
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
The Journal of Machine Learning Research
A framework for kernel-based multi-category classification
Journal of Artificial Intelligence Research
Learning distance functions for image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Probabilistic Models for Inference about Identity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Pairwise classification is the task to predict whether the examples a,b of a pair (a,b) belong to the same class or to different classes. In particular, interclass generalization problems can be treated in this way. In pairwise classification, the order of the two input examples should not affect the classification result. To achieve this, particular kernels as well as the use of symmetric training sets in the framework of support vector machines were suggested. The paper discusses both approaches in a general way and establishes a strong connection between them. In addition, an efficient implementation is discussed which allows the training of several millions of pairs. The value of these contributions is confirmed by excellent results on the labeled faces in the wild benchmark.