Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Recognition of handprinted numerals in VISA card application forms
Machine Vision and Applications
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
One-class svms for document classification
The Journal of Machine Learning Research
Support Vector Data Description
Machine Learning
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Handwritten word recognition with character and inter-character neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Fuzzy one-class classification model using contamination neighborhoods
Advances in Fuzzy Systems
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In one-class classification, the problem is to distinguish one class of data from the rest of the feature space. It is important in many applications where one of the classes is characterized well, while no measurements are available for the other class. Scholkopf et al. first introduced a method of adapting the support vector machine (SVM) methodology to the one-class classification problem, called one-class SVM. In this paper, we incorporate the concept of fuzzy set theory into the one-class SVM. We apply a fuzzy membership to each input point and reformulate the one-class SVM such that different input points can make different contributions to the learning of decision surface. Besides, the parameters to be identified in one-class SVM, such as the components within the weight vector and the bias term, are fuzzy numbers. This integration preserves the benefits of SVM learning theory and fuzzy set theory, where the SVM learning theory characterizes the properties of learning machines which enable them to effectively generalize the unseen data and the fuzzy set theory might be very useful for finding a fuzzy structure in an evaluation system.