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    • nu-Anomica algorithm

    Metadata Updated: April 11, 2025

    One-class nu-Support Vector machine (SVMs) learning technique maps the input data into a much higher dimensional space and then uses a small portion of the training data (support vectors) to parametrize the decision surface that can linearly separate nu fraction of training points (labeled as anomalies) from the rest. The exact solution of standard one-class nu SVMs assigns (at least) nu fraction of training points as support vectors. However some of these support vectors may be unnecessary or redundant. Hence the computational issue turns alarming especially when SVMs based novelty detectors with nonlinear kernels are trained on data sets of huge size. The proposed nu-Anomica algorithm can solve this problem. The idea is to train the machine such that it can provide a close approximation to the exact decision plane using far less number of training points and without loosing much of the generalization performance of the classical approach. The developed procedure closely preserves the accuracy of standard One-class nu-SVMs while reducing both training time and test time by several factors.

    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 November 12, 2020
    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 November 12, 2020
    Metadata Updated Date April 11, 2025
    Publisher Dashlink
    Maintainer
    Identifier DASHLINK_131
    Data First Published 2010-09-10
    Data Last Modified 2025-04-01
    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 d5349a56-aa26-4e94-9d84-8e9ba01ce99a
    Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
    Harvest Source Title NASA Data.json
    Homepage URL https://c3.nasa.gov/dashlink/resources/131/
    Program Code 026:029
    Source Datajson Identifier True
    Source Hash 5ddb85689ffe0ab11c411bec0bc9dbfe4d029b456a6864aa67712225855ad68e
    Source Schema Version 1.1

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