Parents (Total: 5)

The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	6627440
MD5	66104E721852F1E0161A50B89B207C28
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 binaries and examples.
PackageMaintainer	Debian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org>
PackageName	weka
PackageSection	science
PackageVersion	3.6.14-3
SHA-1	413578EA72D0F2A5014C6B028565B5B5B69E7E30
SHA-256	58EA5EC3B42CA9719BE5756D1BD64EBED0E69D16D6FFFA200B3CEBF4CCCCAB16

Key	Value
FileSize	6627580
MD5	4D4324E26B6AA3CEA4F43E20852EFE71
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 binaries and examples.
PackageMaintainer	Debian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org>
PackageName	weka
PackageSection	science
PackageVersion	3.6.14-2
SHA-1	8D665C32D3214D047EADB79AFCE5BA98A0CDFF27
SHA-256	AEA40F356EB5AD9399C7CDBCB87A859F8FF17DF9AAA2A7D46391CC3ABD3F963B

Key	Value
MD5	EA74E950EC5AA4A7ED2034FDA6C84F78
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a data-set or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	2.fc24
PackageVersion	3.6.13
SHA-1	59DABF4634BC1455F0829472D885642D723CB691
SHA-256	43E9B28D52B8E0FDA22EB4285AAD30FB1B571878FF6E71B0661FD2559796AE3A

Key	Value
MD5	8D60C7723EDE350E152877581A1947F0
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a data-set or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
PackageMaintainer	Fedora Project
PackageName	weka
PackageRelease	2.fc24
PackageVersion	3.6.13
SHA-1	DAA2D46E065CE230E1C485F9784F098F3569C010
SHA-256	901DA0DF8A4EDAD93E9C0E8A28C9A13591679F6340FF8CF2AFC7385C5203B86D

Key	Value
FileSize	6626932
MD5	5962D973647F6629A499F480A61657A8
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 binaries and examples.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	weka
PackageSection	science
PackageVersion	3.6.14-2
SHA-1	A8E52AEB690C09A84BBAD39B18C1DFB8A151559D
SHA-256	A381AD43C4B4109F653785EA4EC9FF3603DA5434D21602ACAC5E8F1453E336C5