Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	52FD8EB8EC2348D1E76EF4C3774BD7AE
PackageArch	aarch64
PackageDescription	The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts). For the latter the efficient LINADD optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the "combined kernel" which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden Markov-models. 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 "pre-processors" (e.g. subtracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. This build comes WITHOUT support for Thorsten Joachim's `SVM^light`, because of it's 'no-redistribute', 'no-commercial-use' license. This package contains the ChangeLog, a very detailed documentation, and some great examples for shogun. If you need the Chinese API-docs, you would want to install shogun-doc-cn, too.
PackageMaintainer	Fedora Project
PackageName	shogun-doc
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	6B425709841A847AA3FC7B9406D58797384F2D74
SHA-256	C000A7DBAAA5718314D0FD8C3AFA5BC92E7673CEAB57EEBFC678AF86BD6CBE2D

Key	Value
MD5	95CC96D701048F5A50ED9782C6AABF1C
PackageArch	aarch64
PackageDescription	The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts). For the latter the efficient LINADD optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the "combined kernel" which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden Markov-models. 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 "pre-processors" (e.g. subtracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. This build comes WITHOUT support for Thorsten Joachim's `SVM^light`, because of it's 'no-redistribute', 'no-commercial-use' license. This package contains the documentation files for shogun in Chinese language.
PackageMaintainer	Fedora Project
PackageName	shogun-doc-cn
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	102FFF431D96C919194DCBE874B432A65BA2A499
SHA-256	721AACE41B35270AB9346DF56A18CF1BEE0A216D4C5FA93E791CF6A727BD99B7

Key	Value
MD5	238979F9A06BCB35AC13A0EC7212902F
PackageArch	aarch64
PackageDescription	The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts). For the latter the efficient LINADD optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the "combined kernel" which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden Markov-models. 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 "pre-processors" (e.g. subtracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. This build comes WITHOUT support for Thorsten Joachim's `SVM^light`, because of it's 'no-redistribute', 'no-commercial-use' license. This package contains the ChangeLog, a very detailed documentation, and some great examples for shogun. If you need the Chinese API-docs, you would want to install shogun-doc-cn, too.
PackageMaintainer	Fedora Project
PackageName	shogun-doc
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	A34E8EDB663192FBCD8365C7741B89EF5C161B57
SHA-256	2D3E44CD953AE0DEF8265D9F11BAB16E687E364AF995F54167E7ECA23226ADBA

Key	Value
MD5	2EEC745F9CE248002037106425534DFF
PackageArch	aarch64
PackageDescription	The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art LibSVM. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels as e.g. the Locality Improved, Fischer, TOP, Spectrum, Weighted Degree Kernel (with shifts). For the latter the efficient LINADD optimizations are implemented. Also SHOGUN offers the freedom of working with custom pre-computed kernels. One of its key features is the "combined kernel" which can be constructed by a weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain. An optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Currently SVM 2-class classification and regression problems can be dealt with. However SHOGUN also implements a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and features algorithms to train hidden Markov-models. 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 "pre-processors" (e.g. subtracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. This build comes WITHOUT support for Thorsten Joachim's `SVM^light`, because of it's 'no-redistribute', 'no-commercial-use' license. This package contains the documentation files for shogun in Chinese language.
PackageMaintainer	Fedora Project
PackageName	shogun-doc-cn
PackageRelease	0.33.git20141224.d71e19a.fc22
PackageVersion	3.2.0.1
SHA-1	D576A5B680253D2A8D20604CE15DB3372005A373
SHA-256	67A9978CAFCCABF6932B986BC0F4197B4F946042519ECF629B7651D6AF5288BC