anomaly detection by one-class SVM

PySVM : A NumPy implementation of SVM based on SMO algorithm. Numpy构建SVM分类、回归与单分类，支持缓存机制与随机傅里叶特征

Fast Incremental Support Vector Data Description implemented in Python

A one class svm implementation to detect the anomalies in network.

This demo shows how to detect the crack images using one-class SVM using MATLAB.

Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'

A curated list of awesome resources dedicated to One Class Classification.

Detect outliers with 3 methods: LOF, DBSCAN and one-class SVM

Canned estimators and pre-trained models converted for TensorFlow.

Anomaly detection for Sequential dataset

Anomaly Detection in Optical Networks

OCS-WAF: a Web Application Firewall based on anomaly detection using One-Class SVM classifier

One-Class SVMs for Document Classification

Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

Insight Data Science DS.2019C.TO project

Anomaly detection using IF, LOF, OC-SVM, Autoencoder.

Detecting weather anomalies for Dublin Airport

Detecting Malicious Commands

Machine learning pipelines for anomaly detection using unsupervised learning

Project from seminar "Data Mining in Production"