A framework for structural risk minimisation
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Gene Expression Microarrays for Phenotype Classification
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Journal of the American Society for Information Science and Technology
An introduction to variable and feature selection
The Journal of Machine Learning Research
Automatic Information Organization and Retrieval.
Automatic Information Organization and Retrieval.
Bayesian support vector regression using a unified loss function
IEEE Transactions on Neural Networks
Local similarity discriminant analysis
Proceedings of the 24th international conference on Machine learning
An smo algorithm for the potential support vector machine
Neural Computation
Generative models for similarity-based classification
Pattern Recognition
Linear potential proximal support vector machines for pattern classification
Optimization Methods & Software - Mathematical programming in data mining and machine learning
An improved SVM method P-SVM for classification of remotely sensed data
International Journal of Remote Sensing
Supplier selection based on hierarchical potential support vector machine
Expert Systems with Applications: An International Journal
Learning kernels from indefinite similarities
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The Journal of Machine Learning Research
Matrix pattern based minimum within-class scatter support vector machines
Applied Soft Computing
The dissimilarity space: Bridging structural and statistical pattern recognition
Pattern Recognition Letters
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We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. The new technique, called potential support vector machine (P-SVM), is a large-margin method for the construction of classifiers and regression functions for the column objects. Contrary to standard support vector machine approaches, the P-SVM minimizes a scale-invariant capacity measure and requires a new set of constraints. As a result, the P-SVM method leads to a usually sparse expansion of the classification and regression functions in terms of the row rather than the column objects and can handle data and kernel matrices that are neither positive definite nor square. We then describe two complementary regularization schemes. The first scheme improves generalization performance for classification and regression tasks; the second scheme leads to the selection of a small, informative set of row support objects and can be applied to feature selection. Benchmarks for classification, regression, and feature selection tasks are performed with toy data as well as with several real-world data sets. The results show that the new method is at least competitive with but often performs better than the benchmarked standard methods for standard vectorial as well as true dyadic data sets. In addition, a theoretical justification is provided for the new approach.