Result for 01FD5A57BE8DD331E0C28642B8905D13211B71C6

Query result

Key Value
FileName./usr/share/doc/weka-doc/doc/weka/gui/beans/AbstractTestSetProducer.html
FileSize32804
MD5EB2A993AF5973C1A797CE9467930D759
SHA-101FD5A57BE8DD331E0C28642B8905D13211B71C6
SHA-256A554F42432EAAE0469D9DF8B064E779234EA1F49430F7006009D986237FEEBDE
SSDEEP768:oi2uhPC0RB4ykRUujREcuzD1P1udhrpsjydPxvgRl:t2uHRuID1P1ohaRl
TLSHT100E2D727259B29B306C393DEBF7A1B2576E70058E1625580BFFED32907C4EC56B0924B
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