Search results
Results From The WOW.Com Content Network
It features a collection of classification, regression, concept drift detection and anomaly detection algorithms. It also includes a set of data stream generators and evaluators. scikit-multiflow is designed to interoperate with Python's numerical and scientific libraries NumPy and SciPy and is compatible with Jupyter Notebooks .
Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear rejection or omission from the data to aid statistical analysis, for example to compute the mean or standard deviation.
Anomaly detection: 2020 (continually updated) [329] [330] Iurii D. Katser and Vyacheslav O. Kozitsin On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study Most data files are adapted from UCI Machine Learning Repository data, some are collected from the literature.
Isolation Forest is an algorithm for data anomaly detection using binary trees.It was developed by Fei Tony Liu in 2008. [1] It has a linear time complexity and a low memory use, which works well for high-volume data.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source , under a BSD license . [ 4 ]
A Tsetlin machine is a form of learning automaton collective for learning patterns using propositional logic. Ole-Christoffer Granmo created [ 1 ] and gave the method its name after Michael Lvovitch Tsetlin , who invented the Tsetlin automaton [ 2 ] and worked on Tsetlin automata collectives and games. [ 3 ]
ML.NET is a free software machine learning library for the C# and F# programming languages. [4] [5] [6] It also supports Python models when used together with NimbusML.The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. [7]
Albumentations has gained significant popularity and recognition in the computer vision and deep learning community since its introduction in 2018. The library was designed to provide a flexible and efficient framework for data augmentation in computer vision tasks, and has been widely adopted in academic research, open-source projects, and ...