Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support Vector Data Description
Machine Learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Domain described support vector classifier for multi-classification problems
Pattern Recognition
Bearing fault detection using artificial neural networks and genetic algorithm
EURASIP Journal on Applied Signal Processing
An effective neuro-fuzzy paradigm for machinery condition healthmonitoring
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A theoretical framework for multi-sphere support vector data description
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
New multi-class classification method based on the SVDD model
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Multiple distribution data description learning algorithm for novelty detection
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Incremental threshold learning for classifier selection
Neurocomputing
Hyperdisk based large margin classifier
Pattern Recognition
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We propose two variations of the support vector data description (SVDD) with negative samples (NSVDD) that learn a closed spherically shaped boundary around a set of samples in the target class by involving different forms of slack vectors, including the two-norm NSVDD and ν-NSVDD. We extend the NSVDDs to solve the multiclass classification problems based on the distances between the samples and the centers of the learned spherically shaped boundaries in a kernel-defined feature space by using a combination of linear discriminant analysis (LDA) and nearest-neighbor (NN) rule. Extensive simulations are developed with one real-world data set on the automatic monitoring of roller bearings with vibration signals and eight benchmark data sets for both binary and multiclass classification. The benchmark testing results show that our proposed methods provide lower classification error rates and smaller standard deviations with the cross-validation procedure. The two-norm NSVDD with the LDA-NN rule recorded a test accuracy of 100.0% for the binary fault detection of roller bearings and 99.9% for the multiclass classification of roller bearings under six conditions.