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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. [56] scikit-learn is an open-source Python library that contains some algorithms for unsupervised anomaly detection.

  3. 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 .

  4. 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.

  5. Local outlier factor - Wikipedia

    en.wikipedia.org/wiki/Local_outlier_factor

    In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.

  6. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    This page was last edited on 25 October 2024, at 09:18 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  7. k-SVD - Wikipedia

    en.wikipedia.org/wiki/K-SVD

    In applied mathematics, k-SVD is a dictionary learning algorithm for creating a dictionary for sparse representations, via a singular value decomposition approach. k-SVD is a generalization of the k-means clustering method, and it works by iteratively alternating between sparse coding the input data based on the current dictionary, and updating the atoms in the dictionary to better fit the data.

  8. Waluigi effect - Wikipedia

    en.wikipedia.org/wiki/Waluigi_effect

    In the field of artificial intelligence (AI), the Waluigi effect is a phenomenon of large language models (LLMs) in which the chatbot or model "goes rogue" and may produce results opposite the designed intent, including potentially threatening or hostile output, either unexpectedly or through intentional prompt engineering.

  9. Caffe (software) - Wikipedia

    en.wikipedia.org/wiki/Caffe_(software)

    Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation.It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8]