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  2. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    In the oil and gas sector, anomaly detection is not just crucial for maintenance and safety, but also for environmental protection. [19] Aljameel et al. propose an advanced machine learning-based model for detecting minor leaks in oil and gas pipelines, a task traditional methods may miss. [19]

  3. 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.

  4. Isolation forest - Wikipedia

    en.wikipedia.org/wiki/Isolation_forest

    Active Learning: Incorporating feedback loops to iteratively refine the model using misclassified transactions could improve recall and precision. [ 8 ] Feature Engineering : Adding transaction metadata, such as merchant location and transaction type, could further aid anomaly detection.

  5. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    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. treated for missing values, numerical attributes only, different percentages of anomalies, labels 1000+ files ARFF: Anomaly detection

  6. Hierarchical temporal memory - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_temporal_memory

    Hierarchical temporal memory (HTM) is a biologically constrained machine intelligence technology developed by Numenta.Originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee, HTM is primarily used today for anomaly detection in streaming data.

  7. Probably approximately correct learning - Wikipedia

    en.wikipedia.org/wiki/Probably_approximately...

    An Introduction to Computational Learning Theory. MIT Press, 1994. A textbook. M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of Machine Learning. MIT Press, 2018. Chapter 2 contains a detailed treatment of PAC-learnability. Readable through open access from the publisher. D. Haussler.

  8. Outline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Outline_of_machine_learning

    Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]

  9. Double descent - Wikipedia

    en.wikipedia.org/wiki/Double_descent

    "The m = n Machine Learning Anomaly". Preetum Nakkiran; Gal Kaplun; Yamini Bansal; Tristan Yang; Boaz Barak; Ilya Sutskever (29 December 2021). "Deep double descent: where bigger models and more data hurt".