Parents (Total: 57)

The searched file hash is included in 57 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	985F905EB488F64647F841E671DCF8F6
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	6.fc24
PackageVersion	1.0
SHA-1	096ADC455DCDD18882895C0053AC21CCBB46B6E4
SHA-256	6614BCF50CF7A1D9DF0FFCCE48F4DF68FF161C41602C09B630847F2BE2613FDC

Key	Value
MD5	0025AD59E690306FE4EEFCA726C9E8F2
PackageArch	ppc64le
PackageDescription	This package provides debug information for package tapkee. Debug information is useful when developing applications that use this package or when debugging this package.
PackageMaintainer	Fedora Project
PackageName	tapkee-debuginfo
PackageRelease	3.fc22
PackageVersion	1.0
SHA-1	0CA05D2CF0184A71EA92647450172FBE634DDE12
SHA-256	712B61558E10CA99967B458CADEF730724BB1247F018A903329567F8B6BB5C2B

Key	Value
FileSize	1537158
MD5	C199C94E43ABCEA25ABAC66B31CB5AEE
PackageDescription	Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-7.3
SHA-1	109CF96F11881417AA73B3A0849648C1FD015B86
SHA-256	A74C47E4268DCE64130F1A61D4CCF1A0A203B6C1607B285097817A82480B08AF

Key	Value
FileSize	1538796
MD5	DB4F84D080A258EC2FAA13226C29C70D
PackageDescription	Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Soeren Sonnenburg <sonne@debian.org>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-7.3
SHA-1	13532D4CD2984E2A32D52C95B5D240AE6F4C588C
SHA-256	400CC7EAD2768D0DF18314D45E82F5E0EC73C149A1072EDA2298CC8E4AC89120

Key	Value
MD5	3240C15667DA078EF8EE7311C70BDA2C
PackageArch	ppc64
PackageDescription	This package provides debug information for package tapkee. Debug information is useful when developing applications that use this package or when debugging this package.
PackageMaintainer	Fedora Project
PackageName	tapkee-debuginfo
PackageRelease	3.fc21
PackageVersion	1.0
SHA-1	1693CBD918D96FC62AD336BF0A84DE1921BF209D
SHA-256	06A41A94C4C5AA6BC2CCA8B05C80E8FA29E7BEDF88D3472875FCAF441BD283BC

Key	Value
MD5	D109A3B0490E41B1AEEDD7F6813DAD40
PackageArch	x86_64
PackageDescription	This package provides debug information for package tapkee. Debug information is useful when developing applications that use this package or when debugging this package.
PackageMaintainer	Fedora Project
PackageName	tapkee-debuginfo
PackageRelease	2.el7
PackageVersion	1.1
SHA-1	183AEC99144D278C39ACDF56097E60F24A67A438
SHA-256	3C195437BEDF258C1962C78852AA0011C60CC5A3247075C5DB99FBBB2C9AE326

Key	Value
FileSize	609770
MD5	3C85B3541B258A22A6953C63DAC396F1
PackageDescription	Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-7.3build4
SHA-1	1F22CBFA9FB2508E4CF3BBE49A11267B0C184486
SHA-256	5A1968EEE5F7C8788E25141FC65FDEE43D49409392F566DA715130CC14E7DE0F

Key	Value
MD5	89A2BB1637C591F276519840B0FDEB05
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	3.fc21
PackageVersion	1.0
SHA-1	1FC7803F82A70E0F6EF722F6DBC1903403C3D6E9
SHA-256	2F11CC7925AADB52FC5EC712A9C0D2D60A895D711844FC91B4A15CD7575F29E0

Key	Value
FileSize	1531732
MD5	06695F9BF4BCB387C47CCDC745E194D2
PackageDescription	Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables.
PackageMaintainer	Debian QA Group <packages@qa.debian.org>
PackageName	libshogun-dev
PackageSection	libdevel
PackageVersion	3.2.0-8+b1
SHA-1	220EEBC461C6624F4E1BBE39BA99C4C3112E3392
SHA-256	6170315C72691DC3E5751A8A7F04DD60C79AB199DC920EA40DE0904430BB76A4

Key	Value
MD5	6C88DD88E14EA71665ED6F29F8342E5A
PackageArch	noarch
PackageDescription	Tapkee is a C++ template library for dimensionality reduction with some bias on spectral methods. The Tapkee origins from the code developed during GSoC 2011 as the part of the Shogun machine learning toolbox. The project aim is to provide efficient and flexible standalone library for dimensionality reduction which can be easily integrated to existing codebases. Tapkee leverages capabilities of effective Eigen3 linear algebra library and optionally makes use of the ARPACK eigensolver. The library uses CoverTree and VP-tree data-structures to compute nearest neighbors. To achieve greater flexibility we provide a callback interface which decouples dimension reduction algorithms from the data representation and storage schemes. Tapkee provides implementations of the following dimension reduction methods: * Locally Linear Embedding and Kernel Locally Linear Embedding (LLE/KLLE) * Neighborhood Preserving Embedding (NPE) * Local Tangent Space Alignment (LTSA) * Linear Local Tangent Space Alignment (LLTSA) * Hessian Locally Linear Embedding (HLLE) * Laplacian eigenmaps * Locality Preserving Projections * Diffusion map * Isomap and landmark Isomap * Multidimensional scaling and landmark Multidimensional scaling (MDS/lMDS) * Stochastic Proximity Embedding (SPE) * PCA and randomized PCA * Kernel PCA (kPCA) * Random projection * Factor analysis * t-SNE * Barnes-Hut-SNE
PackageMaintainer	Fedora Project
PackageName	tapkee-devel
PackageRelease	2.el7
PackageVersion	1.1
SHA-1	233E1FF4B960B84281649C9D7762711E4A45E2B7
SHA-256	39C283D96740071F4111C64DCDD6319D2C95BF07ACBC498289D77D1D401E2B38