Python code for abnormal detection using Support Vector Data Description (SVDD)
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Updated
Jun 17, 2024 - Python
Python code for abnormal detection using Support Vector Data Description (SVDD)
Safety regions research is a well-known task for ML and the main focus is to avoid false positives, i.e., including in the safe region unsafe points. In this repository, two methods for the research of zero FPR regions are proposed: the first one is based simply on the reduction of the SVDD radius until only safe points are enclosed in the SVDD …
MATLAB Code for abnormal detection using Support Vector Data Description (SVDD).
A Julia package for one-class classification sampling methods.
Scripts and notebooks to reproduce the experiments and analyses of the paper Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm, "Efficient SVDD sampling with approximation guarantees for the decision boundary", Machine Learning (2022).
Subspace Support Vector Data Description
A Julia package for Support Vector Data Description.
Thesis work on Video Anomaly Detection
Anomaly detection for deep SVDD
Fast Incremental Support Vector Data Description implemented in Python
Hyperparameter selection of one-class support vector machine by self-adaptive data shifting
Decision Boundaries Visualization of SVDD (libsvm-3.23)
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