Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
LDBOD: A novel local distribution based outlier detector
Pattern Recognition Letters
Review: A new training method for support vector machines: Clustering k-NN support vector machines
Expert Systems with Applications: An International Journal
The data complexity index to construct an efficient cross-validation method
Decision Support Systems
Fuzzy semi-supervised support vector machines
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
An optimization model for outlier detection in categorical data
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
One-Class classification through optimized feature boundaries detection and prototype reduction
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
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This paper proposes the modified support vector novelty detector (SVND) for novelty detection which addresses the problem of detecting outliers from normal data patterns. While the original SVND [Neural Comput. 13 (2001) 1443] attempts to estimate a function to separate the region of normal data patterns from that of outliers based on normal data patterns, the modified SVND generalizes it to take into account the outliers in the training set by separating both the normal data patterns and the outliers from the origin with maximal margin. By examining several artificial and real data sets, the experiment shows that there is significant improvement in the performance of the modified SVND in comparison with the original SVND. Furthermore, the original SVND is sensitive to the outliers, with the performance deteriorating when outliers are included in the training set.