anomaly detection by one-class SVM

Fast Incremental Support Vector Data Description implemented in Python

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

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

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

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

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

One-Class SVMs for Document Classification

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

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

Detecting weather anomalies for Dublin Airport

Anomaly Detection in Optical Networks

Detecting Malicious Commands

Project from seminar "Data Mining in Production"

Data exploration, anomaly detection, and data generation for oil deposits dataset.

The project explores a range of methods, including both statistical analysis, traditional machine learning and deep learning approaches to anomaly detection a critical aspect of data science and machine learning, with a specific application to the detection of credit card fraud detection and prevention.