Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Robust Classification for Imprecise Environments
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Regularization and statistical learning theory for data analysis
Computational Statistics & Data Analysis - Nonlinear methods and data mining
A robust minimax approach to classification
The Journal of Machine Learning Research
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Support Vector Data Description
Machine Learning
A tutorial on support vector regression
Statistics and Computing
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Decision making under uncertainty using imprecise probabilities
International Journal of Approximate Reasoning
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Robust support vector machines for classification and computational issues
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
Soft clustering using weighted one-class support vector machines
Pattern Recognition
Gaussian kernel optimization for pattern classification
Pattern Recognition
ACM Computing Surveys (CSUR)
Robust support vector machine training via convex outlier ablation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Robustness and Regularization of Support Vector Machines
The Journal of Machine Learning Research
A survey of recent trends in one class classification
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
A Class of Single-Class Minimax Probability Machines for Novelty Detection
IEEE Transactions on Neural Networks
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A novelty detection robust model is studied in the paper. It is based on contaminated robust models which produce a set of probability distributions of data points instead of the empirical distribution. The minimax and minimin strategies are used to construct optimal separating functions. An algorithm for computing the optimal parameters of the novelty detection model is reduced to a finite number of standard SVM tasks with weighted data points. Experimental results with synthetic and some real data illustrate the proposed novelty detection robust model.