Parents (Total: 10)

The searched file hash is included in 10 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	4741684
MD5	D03BBD5911AA088145547AE1D9410E90
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	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	weka-doc
PackageSection	doc
PackageVersion	3.6.10-2
SHA-1	0DCB2C9F7011EBB669E1187794E206613866FBBF
SHA-256	24B836F62CCB7CFCDF8BAE300165E1DD6FD9D89FBD18DFA248BE22876C9A2A17

Key	Value
FileSize	4763266
MD5	3972925652915ED8C857BAE63A9BB3F5
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	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	weka-doc
PackageSection	doc
PackageVersion	3.6.13-1
SHA-1	89FCB64503135EDBD6598C3F272087F47329AFA8
SHA-256	5417A5EDCE4660F90B96705541EAA06317E51B0CF98734A789207D493ED6FFC2

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

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	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	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

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