@inproceedings{537f54350fdb4e81ae7bfafd466f0e1a,
title = "Fast Kernel-based Method for Anomaly Detection",
abstract = "Anomaly detection (AD) involves detecting abnormality from normality and has a wide spectrum of applications in reality. Kernel-based methods for AD have been proven robust with diverse data distributions and offering good generalization ability. Stochastic gradient descent (SGD) method has recently emerged as a promising framework to devise ultra-fast learning methods. In this paper, we conjoin the advantages of Kernel-based method and SGD-based method to propose fast learning methods for anomaly detection. We validate the proposed methods on 8 benchmark datasets in UCI repository and KDD cup 1999 dataset. The experimental results show that the proposed methods offer a comparable one-class classification accuracy while simultaneously achieving a significantly computational speed-up",
keywords = "Anomaly detection, Kernel method, Stochastic algorithm",
author = "Anh Le and Trung Le and Khanh Nguyen and Van Nguyen and Thai Le and Dat TRAN",
year = "2016",
doi = "10.1109/ijcnn.2016.7727609",
language = "English",
isbn = "9781509006212",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "3211--3217",
editor = "Chen, {Hussein A. Abbass Huanhuan}",
booktitle = "2016 International Joint Conference on Neural Networks, IJCNN 2016",
address = "United States",
note = "2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
}