Selecting training points for one-class support vector machines

Authors:
Yuhua Li
Affiliations:
School of Computing and Intelligent Systems, University of Ulster, Londonderry, BT48 7JL, UK
Venue:
Pattern Recognition Letters
Year:
2011

Citing 17
Cited 3

Support vector domain description

Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An Algorithm for Finding Best Matches in Logarithmic Expected Time

ACM Transactions on Mathematical Software (TOMS)
Computing location depth and regression depth in higher dimensions

Statistics and Computing
Estimating the Support of a High-Dimensional Distribution

Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research
Neighborhood Property--Based Pattern Selection for Support Vector Machines

Neural Computation
Learning Minimum Volume Sets

The Journal of Machine Learning Research
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG

The Journal of Machine Learning Research
Reducing examples to accelerate support vector regression

Pattern Recognition Letters
Selecting valuable training samples for SVMs via data structure analysis

Neurocomputing
High-order Markov kernels for intrusion detection

Neurocomputing
The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition
Efficient nearest neighbor query based on extended B+-tree in high-dimensional space

Pattern Recognition Letters
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Training data selection for support vector machines

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Using One-Class SVMs and Wavelets for Audio Surveillance

IEEE Transactions on Information Forensics and Security
k-nearest-neighbor Bayes-risk estimation

IEEE Transactions on Information Theory

Support vector machines training data selection using a genetic algorithm

SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Convex and concave hulls for classification with support vector machine

Neurocomputing
Fast classification for large data sets via random selection clustering and Support Vector Machines

Intelligent Data Analysis

Quantified Score

Hi-index	0.10

Visualization

Abstract

This paper proposes a training points selection method for one-class support vector machines. It exploits the feature of a trained one-class SVM, which uses points only residing on the exterior region of data distribution as support vectors. Thus, the proposed training set reduction method selects the so-called extreme points which sit on the boundary of data distribution, through local geometry and k-nearest neighbours. Experimental results demonstrate that the proposed method can reduce training set considerably, while the obtained model maintains generalization capability to the level of a model trained on the full training set, but uses less support vectors and exhibits faster training speed.