| Key | Value |
|---|---|
| FileName | ./usr/share/doc/weka-doc/doc/weka/attributeSelection/RankSearch.html |
| FileSize | 29648 |
| MD5 | 2B42419FD94AEE0915CA692D841B0069 |
| SHA-1 | 0009364732D112E417784C3D373A9A3BD89EDA43 |
| SHA-256 | 5A9B8681402346A9CF199E6F1C7377ABED26F059350993133533B0BFDB40CC12 |
| SSDEEP | 384:YXPOFicikNJe63hXWBtlJsXbXD11GbPwvZicikNI:YXPO0R9BtrsXbXD11IPwvgR1 |
| TLSH | T155D2B539209737B70E1B81CE85F92AA177F6886DD1112844B6FDC72E17C9E81BA2354F |
| 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 | 4773034 |
| MD5 | A80C6D391FCD9DA3F5C470090E3BFB10 |
| 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 | 3.6.14-1 |
| SHA-1 | 05AD641B6678E7C3013A36E6A0D270C660496875 |
| SHA-256 | D2CD69B7451710481D83C5DBAE746A4ECDDAECF0F3B879ED7763DBE0885875BA |
| Key | Value |
|---|---|
| FileSize | 4773760 |
| MD5 | F762C2285A8EFEFCDBE7B3B2E731050A |
| 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 | 3.6.14-1 |
| SHA-1 | 035BB36EF2ADD95137024A6E4092B2A77D6090FB |
| SHA-256 | 53B621118EF773E031264740267EC072418EF8CB90D9D554DA90BEA6948D895E |