Digital signal processing (2nd ed.): principles, algorithms, and applications
Digital signal processing (2nd ed.): principles, algorithms, and applications
Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Outlier Detection Using Classifier Instability
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Gear crack detection using kernel function approximation
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Maximum Margin One Class Support Vector Machines for multiclass problems
Pattern Recognition Letters
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For good classification preprocessing is a key step. Good pre-processing reduces the noise in the data and retains most information needed for classification. Poor preprocessing on the other hand can make classification almost impossible. In this paper we evaluate several feature extraction methods in a special type of outlier detection problem, machine fault detection. We will consider measurements on water pumps under both normal and abnormal conditions. We use a novel data description method, called the Support Vector Data Description, to get an indication of the complexity of the normal class in this data set and how well it is expected to be distinguishable from the abnormal data.