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  2. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    ELKI is an open-source Java data mining toolkit that contains several anomaly detection algorithms, as well as index acceleration for them. PyOD is an open-source Python library developed specifically for anomaly detection. [52] scikit-learn is an open-source Python library that contains some algorithms for unsupervised anomaly detection.

  3. Tsetlin machine - Wikipedia

    en.wikipedia.org/wiki/Tsetlin_machine

    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]

  4. One-class classification - Wikipedia

    en.wikipedia.org/wiki/One-class_classification

    The term one-class classification (OCC) was coined by Moya & Hush (1996) [8] and many applications can be found in scientific literature, for example outlier detection, anomaly detection, novelty detection. A feature of OCC is that it uses only sample points from the assigned class, so that a representative sampling is not strictly required for ...

  5. Isolation forest - Wikipedia

    en.wikipedia.org/wiki/Isolation_forest

    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.

  6. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference from many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model ...

  7. Unsupervised learning - Wikipedia

    en.wikipedia.org/wiki/Unsupervised_learning

    Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision.

  8. U-Net - Wikipedia

    en.wikipedia.org/wiki/U-Net

    U-Net is a convolutional neural network that was developed for image segmentation. [1] The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation.

  9. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.