Parents (Total: 8)

The searched file hash is included in 8 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
FileSize	7218182
MD5	06D48686F139062C279D8E87C6648FEE
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	3.6.13-1
SHA-1	9ED5CEF0A52B33F3F967E5D418C219C5759319F6
SHA-256	AC823BD17B2D411B4A251CFFBAAB8EB483D006833C2729BEB5190B9697CD3446

Key	Value
FileSize	7152380
MD5	146A5444304D329D1FFA210C62614C8C
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	3.6.11-1
SHA-1	C9F4C32D1FF89528B4E6BD1802C8A06FBC262ABF
SHA-256	DD4C6A20507E158944E5137BA9AA805CA07441C77F375FE8A1A98CD9EFBFC021

Key	Value
FileSize	7247438
MD5	D0134106C97DA329E2FED7C25A6F61E3
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	3.6.14-1
SHA-1	087ED400830970EE83AEB6D20C3F5A428F0554A5
SHA-256	288607FBC9583C52A17964F249184AB9B56A35212FE065AAC5676BCC60C7CF49

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

Key	Value
FileSize	7145326
MD5	7C98FB6232B3BA5FD72E6C0C6A2161D2
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	3.6.10-2
SHA-1	4D21D1A468DCFAAF2A6B341C55ABEA6EF9B182EC
SHA-256	9A40CC6BF0699266C8E050985D3B4948736FFE297F9AA0A0E52C10B73EF26A06

Key	Value
FileSize	7246694
MD5	AA04C61E29293F93DD355197F5D788DA
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	3.6.14-1
SHA-1	74670066A94D07AB8A5E88608BBC031BAEAC9BD6
SHA-256	1F6CCFCE837B05A26C7A937E629CCAD85A393C020B12ECA91B8B8245F54670FA