Result for 0127632D05DC779157959C1EB1422FEDB75435CB

Query result

Key Value
FileName./usr/share/doc/weka-doc/doc/weka/datagenerators/classifiers/regression/package-frame.html
FileSize950
MD561CC5DD39D9911F8D7E6395E1804D9A2
SHA-10127632D05DC779157959C1EB1422FEDB75435CB
SHA-256662642CF5402C2FC494DB2CFAE23100F8BC4C45050127929B8C6A0F90A48935A
SSDEEP24:WpOGN86fGRAV4NyRph5mKNTDdFGbNdmk2UsbNdms7:OSRdNopmqdkfmkjsfmc
TLSHT17C11D4151615BC310A136A80FDA85A84777157D1F6C8DD4EB1FDB12AE7409C88C22299
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
FileSize4773034
MD5A80C6D391FCD9DA3F5C470090E3BFB10
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
PackageVersion3.6.14-1
SHA-105AD641B6678E7C3013A36E6A0D270C660496875
SHA-256D2CD69B7451710481D83C5DBAE746A4ECDDAECF0F3B879ED7763DBE0885875BA
Key Value
FileSize4773760
MD5F762C2285A8EFEFCDBE7B3B2E731050A
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
PackageVersion3.6.14-1
SHA-1035BB36EF2ADD95137024A6E4092B2A77D6090FB
SHA-25653B621118EF773E031264740267EC072418EF8CB90D9D554DA90BEA6948D895E