| Key | Value |
|---|---|
| FileName | ./usr/share/doc/weka-doc/doc/weka/gui/beans/BatchClustererListener.html |
| FileSize | 7881 |
| MD5 | 6856C98B90702B1AAEF00181EA3363C1 |
| SHA-1 | 013B85BABB0691372270C3234ACEE393D9707095 |
| SHA-256 | 2D6C16CA7C3901678648B7A8A2B22C75015D6CEF78F1F0BFBBC242FABCBA7DB0 |
| SSDEEP | 192:cfSXfaFicibYf/4M7AdP0LdTh8Br3p1pOvZicibYf/B:TCFiciUH4tdspWBvsvZiciUHB |
| TLSH | T147F15220185A34360A5793E9BDB90BB53AD34463E5345ED1B2FCC63A2AC7FC53E01A4E |
| hashlookup:parent-total | 1 |
| hashlookup:trust | 55 |
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 |
|---|---|
| FileSize | 4758698 |
| MD5 | 395F555A6E718A6B2C0E1DE71592AADD |
| PackageDescription | Machine 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. |
| PackageMaintainer | Debian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org> |
| PackageName | weka-doc |
| PackageSection | doc |
| PackageVersion | 3.6.11-1 |
| SHA-1 | CFDCD26A0ED845AFABE8E92F027D15660E249381 |
| SHA-256 | 015228BF7DC31378600CC3329219DA0788DBB4799C6C4275B305D90DC84F3D80 |