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| FileName | ./usr/share/doc/weka-doc/doc/weka/gui/boundaryvisualizer/BoundaryVisualizer.html |
| FileSize | 32064 |
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| SHA-1 | 02EFA224DED9A797EC0A05FA49E9142837CB69E4 |
| SHA-256 | 2F28667E90F889DC3CD3F88375A7D274A2035A62B961B473698199882133AA85 |
| SSDEEP | 768:yi2u1gPC0RBXWkRUujRMcuz2D11U9fXhrpnLyjGgjPxvgRl:X2u0RBs2D11U9PhwKRl |
| TLSH | T187E2E736656B2877069782CDBB7E1763B3E70068E2625804FEFDD32913C4EC6560629B |
| hashlookup:parent-total | 2 |
| hashlookup:trust | 60 |
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 |
|---|---|
| FileSize | 5494416 |
| MD5 | 462631619AC4C6E4819F2FACA733D485 |
| PackageDescription | documentation 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. |
| PackageMaintainer | Debian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org> |
| PackageName | weka-doc |
| PackageSection | doc |
| PackageVersion | 3.6.14-2 |
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| FileSize | 5493508 |
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| PackageDescription | documentation 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. |
| PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
| PackageName | weka-doc |
| PackageSection | doc |
| PackageVersion | 3.6.14-2 |
| SHA-1 | 3F9E10B43C21ED9D66CF02CC1808C0A200694264 |
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