High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar, S Karunasekera, C Leckie - Pattern Recognition, 2016 - Elsevier
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …
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High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar, S Karunasekera… - Pattern Recognition, 2016 - infona.pl
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar, S Karunasekera… - Pattern …, 2016 - dl.acm.org
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar, S Karunasekera, C Leckie - Pattern Recognition, 2016 - Elsevier
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …

[引用][C] High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar, S Karunasekera… - Pattern …, 2016 - cir.nii.ac.jp
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep
learning | CiNii Research CiNii 国立情報学研究所 学術情報ナビゲータ[サイニィ] 詳細へ移動 検索 …

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar… - Pattern …, 2016 - ui.adsabs.harvard.edu
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar, S Karunasekera, C Leckie - 2016 - dro.deakin.edu.au
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

SM Erfani, S Rajasegarar, S Karunasekera, C Leckie - 2016 - dro.deakin.edu.au
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …