Result for 01A9AD308F086B9FDDA5AAC45DC63EB711A842CC

Query result

Key Value
FileName./usr/share/doc/weka-doc/doc/weka/classifiers/rules/DTNB.html
FileSize35786
MD548456F4F7E743AC2FA5107F3792B6FC5
SHA-101A9AD308F086B9FDDA5AAC45DC63EB711A842CC
SHA-256FEF572D4F743B1321CADC13A1260FD010AFC3686599AE8FE1CF0DB20D0311FAD
SSDEEP384:SGFicimOJ4B6oeGttXEUdSIhdQADAqdxYMcBy5W06AHtRWcEBtHyetXceT+g8vZy:SG0RlIzggxYMcBy5WZcRWPT+g8vgR/
TLSHT1BAF2972021A33D76195741CED8AC1FAB3BE3C8A5FA002D6574FC93725A85E91DB72D0B
hashlookup:parent-total1
hashlookup:trust55

Network graph view

Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize4763266
MD53972925652915ED8C857BAE63A9BB3F5
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 documentation.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNameweka-doc
PackageSectiondoc
PackageVersion3.6.13-1
SHA-189FCB64503135EDBD6598C3F272087F47329AFA8
SHA-2565417A5EDCE4660F90B96705541EAA06317E51B0CF98734A789207D493ED6FFC2