Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A robust minimax approach to classification
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Top 10 algorithms in data mining
Knowledge and Information Systems
Methods of extremal grouping of the fractional parameters
Automation and Remote Control
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Introduction to Pattern Recognition: A Matlab Approach
Introduction to Pattern Recognition: A Matlab Approach
Regression analysis using the imprecise Bayesian normal model
International Journal of Data Analysis Techniques and Strategies
A machine learning algorithm for classification under extremely scarce information
International Journal of Data Analysis Techniques and Strategies
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This paper presents a model of classification under incomplete information in the form of mathematical expectations of features; it is based on the minimax (minimin) strategy of decision making. The discriminant function is calculated by maximization (minimization) of the risk functional as a measure of misclassification, by a set of distributions of probabilities with bounds determined by information on features, and minimization by the set of parameters. The algorithm is reduced to solution of the parametric problem of linear programming.