Anomaly Detection in Optical Networks
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Updated
Feb 15, 2024 - Jupyter Notebook
Anomaly Detection in Optical Networks
Comparison of various anomaly detection algorithms using scikit-learn and visualization through Plotly Dash
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
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.
This repository provides some recommender engine models.
OCS-WAF: a Web Application Firewall based on anomaly detection using One-Class SVM classifier
Anomaly detection using IF, LOF, OC-SVM, Autoencoder.
Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'
Detect outliers with 3 methods: LOF, DBSCAN and one-class SVM
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.
This demo shows how to detect the crack images using one-class SVM using MATLAB.
Anomaly detection for Sequential dataset
A curated list of awesome resources dedicated to One Class Classification.
One-Class SVMs for Document Classification
Project from seminar "Data Mining in Production"
Data exploration, anomaly detection, and data generation for oil deposits dataset.
Detecting weather anomalies for Dublin Airport
Fast Incremental Support Vector Data Description implemented in Python
anomaly detection by one-class SVM
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