Result for 3BAF5498140E252038CA61A61E984F2E77C97190

Query result

Key Value
FileName./usr/share/doc/weka/changelog.Debian.gz
FileSize1867
MD5B17C2EFD5E0413A41583EABF2E9CBBE9
SHA-13BAF5498140E252038CA61A61E984F2E77C97190
SHA-256954615C6565C5CBADA4FCBC680A8BD875CCD39541F9D16CEE09A6030F7E23A0B
SSDEEP48:XDH0acjr0t1ajT+ZQHBwdloI5xJoFFI4JYxyC:T1c0t1iHBwdf8ILyC
TLSHT11D3117F136777CB4D208C86B489706015E61C0060DF880ADA849AB98C722AB4F06862D
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
FileSize6627580
MD54D4324E26B6AA3CEA4F43E20852EFE71
PackageDescriptionMachine learning algorithms for data mining tasks 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 binaries and examples.
PackageMaintainerDebian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org>
PackageNameweka
PackageSectionscience
PackageVersion3.6.14-2
SHA-18D665C32D3214D047EADB79AFCE5BA98A0CDFF27
SHA-256AEA40F356EB5AD9399C7CDBCB87A859F8FF17DF9AAA2A7D46391CC3ABD3F963B