Parents (Total: 22)

The searched file hash is included in 22 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	FB0FDC148881D2057ED6FD3B3B86138D
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset 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	1.fc18
PackageVersion	3.6.8
SHA-1	0B8CB68B514D3C4025099E0DB6A462AAED99D026
SHA-256	3D35131B632B13C0EEE5EE12D6A525DE6C4F90FEF6ED1F3839056C2AA131FB6A

Key	Value
MD5	3E729A70F089D3A2D172340F3C5596CE
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset 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	1.fc18
PackageVersion	3.6.8
SHA-1	38CE31307DA5279F71EEB195D526690BA3B04754
SHA-256	88FD166B8C2DDA537315061B89D2E28612147FB37D805110A0F6F829CFC1069C

Key	Value
MD5	5C59A92767C1A29798188DA0EA0BF204
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset 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	4.fc21
PackageVersion	3.6.8
SHA-1	3B0CF102EBA4973CCD9581B0EDDBC5F3452E36F7
SHA-256	9C1B0DBE449D56866C95B290A7B1DB976F0CC1F5D6B3D893A64920AFECC378FA

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
MD5	F8ECCCB2C60182DEE962C708AD1428D3
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset 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	5.fc23
PackageVersion	3.6.8
SHA-1	490362BB050D4F4F4099C962EC3664270397157F
SHA-256	0B77E79ACB6EB167DDE70BDD25D8EABA1BF085BCFCD510A45476E0A30E02FB5E

Key	Value
MD5	FFE5F74D9B859E0DE0FD6CE84F8ABB78
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset 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	4.fc21
PackageVersion	3.6.8
SHA-1	51D4E7F1F480CF75BF26A21EEC6735A477EA8C58
SHA-256	9163B9492801A833EB068B42725FE300FB9994DE3BAA416F71890B51C5A76C5B

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	CD0408AAA1278EB00772B57B3B486E94
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset 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	4.fc21
PackageVersion	3.6.8
SHA-1	629B06A9D4ABCDFD85197F2D9EC019584B6ED858
SHA-256	274128B950462B9FC9525F2720AA541AABF47CCE3CF15841AFAB8583DA800153

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	57916F9F0F22747C82146D04D3A07337
PackageArch	noarch
PackageDescription	Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset 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	1.fc18
PackageVersion	3.6.8
SHA-1	93C62B0C55ECDD9C1FCA96FE2363B7178A1CAD60
SHA-256	696C09804D21839E9985A09D6B930AD401C63C9FF87C363B4EA6EA1630992F0E