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    • This is a Non-Federal dataset covered by different Terms of Use than Data.gov.

    nu-Anomica algorithm

    Metadata Updated: February 21, 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. Non-Federal: This dataset is covered by different Terms of Use than Data.gov. License: No license information was provided.

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    Dates

    Metadata Created Date February 21, 2025
    Metadata Updated Date February 21, 2025
    Data Update Frequency irregular

    Metadata Source

    Harvested from nasa test json

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date February 21, 2025
    Metadata Updated Date February 21, 2025
    Publisher Dashlink
    Maintainer
    Identifier DASHLINK_131
    Data First Published 2010-09-10
    Data Last Modified 2025-02-19
    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 0a681b63-ba2f-4277-a18e-bb89ac0787f4
    Harvest Source Id a73e0c30-4684-40ef-908e-d22e9e9e5f86
    Harvest Source Title nasa test json
    Homepage URL https://c3.nasa.gov/dashlink/resources/131/
    Program Code 026:029
    Source Datajson Identifier True
    Source Hash 4699b965f294824d8baa6984ddc8b6e5031a1154b879373231c3a8eba864a1e7
    Source Schema Version 1.1

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