Result for 0033743563973A2EC602B19B42329E050923B69A

Query result

Key Value
FileName./usr/share/doc/weka-doc/doc/weka/classifiers/rules/JRip.Antd.html
FileSize17429
MD533FD35E799CA128A462AE91EADF2B318
SHA-10033743563973A2EC602B19B42329E050923B69A
SHA-256BE1E010A7C49234727A0B4BAEE7B72369D78679D4361E79287FB6C41496169F8
SSDEEP384:GZi2lA7XGPCFiciPNMfWu6+ftLuJRQuzD1x11v7nof/abPxvZiciPNE:yi2ujGPC0RylIRQuzD1P1v7nof/abPxj
TLSHT12572723A09E73877465792C9FABD1E66BAE70459E2211C04BEFCE7362780FC5690510B
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize5494416
MD5462631619AC4C6E4819F2FACA733D485
PackageDescriptiondocumentation for the Weka machine learning suite Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes. . Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets. . This package contains the documentation.
PackageMaintainerDebian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org>
PackageNameweka-doc
PackageSectiondoc
PackageVersion3.6.14-2
SHA-1600200DCE8BDA4D283868645941D907DCD9B7373
SHA-2568361ECEE91A0D59C84CCA6CFD5F869673ADBD325E01CB19E4863AF54F9541FE6
Key Value
FileSize5493508
MD51D0354D28800071DA8401B87DD2BE7FA
PackageDescriptiondocumentation for the Weka machine learning suite Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes. . Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets. . This package contains the documentation.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNameweka-doc
PackageSectiondoc
PackageVersion3.6.14-2
SHA-13F9E10B43C21ED9D66CF02CC1808C0A200694264
SHA-256946C2432DEB84450B8F029A50061A4986FC8EFA1DC76415432DCF5CEBB04F3E3