A Robust Information Clustering Algorithm
Neural Computation
Pattern recognition with SVM and dual-tree complex wavelets
Image and Vision Computing
Computer Vision and Image Understanding
A rough margin based support vector machine
Information Sciences: an International Journal
A Fuzzy support vector classifier based on Bayesian optimization
Fuzzy Optimization and Decision Making
Expert Systems with Applications: An International Journal
Fuzzy multi-class classifier based on support vector data description and improved PCM
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
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
Support vector classifier based on fuzzy c-means and Mahalanobis distance
Journal of Intelligent Information Systems
A novel robust kernel for visual learning problems
Neurocomputing
Detection of anomalous insiders in collaborative environments via relational analysis of access logs
Proceedings of the first ACM conference on Data and application security and privacy
Fuzzy SVM with a New Fuzzy Membership Function to Solve the Two-Class Problems
Neural Processing Letters
Information Sciences: an International Journal
Enhancing one-class support vector machines for unsupervised anomaly detection
Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
Asymmetric least squares support vector machine classifiers
Computational Statistics & Data Analysis
Review: A review of novelty detection
Signal Processing
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This paper proposes a robust support vector machine for pattern classification, which aims at solving the over-fitting problem when outliers exist in the training data set. During the robust training phase, the distance between each data point and the center of class is used to calculate the adaptive margin. The incorporation of the average techniques to the standard support vector machine (SVM) training makes the decision function less detoured by outliers, and controls the amount of regularization automatically. Experiments for the bullet hole classification problem show that the number of the support vectors is reduced, and the generalization performance is improved significantly compared to that of the standard SVM training.