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    • ν-Anomica: A Fast Support Vector based Novelty Detection Technique

    Metadata Updated: April 11, 2025

    In this paper we propose ν-Anomica, a novel anomaly detection technique that can be trained on huge data sets with much reduced running time compared to the benchmark one-class Support Vector Machines algorithm. In ν-Anomica, the idea is to train the machine such that it can provide a close approximation to the exact decision plane using fewer training points and without losing much of the generalization performance of the classical approach. We have tested the proposed algorithm on a variety of continuous data sets under different conditions. We show that under all test conditions the developed procedure closely preserves the accuracy of standard oneclass Support Vector Machines while reducing both the training time and the test time by 5 − 20 times.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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    Dates

    Metadata Created Date December 1, 2022
    Metadata Updated Date April 11, 2025
    Data Update Frequency irregular

    Metadata Source

    Harvested from NASA Data.json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date December 1, 2022
    Metadata Updated Date April 11, 2025
    Publisher Dashlink
    Maintainer
    Identifier DASHLINK_259
    Data First Published 2010-11-17
    Data Last Modified 2025-03-31
    Public Access Level public
    Data Update Frequency irregular
    Bureau Code 026:00
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id 4c6d6cf3-558c-45e4-ab7d-08229599c1c7
    Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
    Harvest Source Title NASA Data.json
    Homepage URL https://c3.nasa.gov/dashlink/resources/259/
    Program Code 026:029
    Source Datajson Identifier True
    Source Hash 8d0066258a38cdeed9dfde8389308322f4c882c8c717f44e447a79654d9fce17
    Source Schema Version 1.1

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