Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
One-class svms for document classification
The Journal of Machine Learning Research
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Support Vector Data Description
Machine Learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
A support vector machine formulation to PCA analysis and its kernel version
IEEE Transactions on Neural Networks
Pruning error minimization in least squares support vector machines
IEEE Transactions on Neural Networks
Comments on “Pruning Error Minimization in Least Squares Support Vector Machines”
IEEE Transactions on Neural Networks
One-class classification with gaussian processes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
On feature selection with principal component analysis for one-class SVM
Pattern Recognition Letters
Multi-output least-squares support vector regression machines
Pattern Recognition Letters
One-class classification with Gaussian processes
Pattern Recognition
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In this paper, we reformulate a standard one-class SVM (support vector machine) and derive a least squares version of the method, which we call LS (least squares) one-class SVM. The LS one-class SVM extracts a hyperplane as an optimal description of training objects in a regularized least squares sense. One can use the distance to the hyperplane as a proximity measure to determine which objects resemble training objects better than others. This differs from the standard one-class SVMs that detect which objects resemble training objects. We demonstrate the performance of the LS one-class SVM on relevance ranking with positive examples, and also present the comparison with traditional methods including the standard one-class SVM. The experimental results indicate the efficacy of the LS one-class SVM.