Result for 007395E4FB3A68384BA3DFC5C598272BBE283255

Query result

Key Value
FileName./usr/share/doc/shogun/html_cn/LaplacianInferenceBase_8cpp.html
FileSize5939
MD5FD3FD3D9FD462DA6074872A2202DDAB0
SHA-1007395E4FB3A68384BA3DFC5C598272BBE283255
SHA-256647A1A1A2C063897504E96AF1B591099E1A8111B95BD854EE98D322C04FE0618
SSDEEP96:xruTsRpRu9bL1sowW5+pIDs6xuj8C8f89lTnuyWw:BuTqpO/kssJTnuyp
TLSHT178C142665C53033381BB41CBB6F3E735B0C190A9CB045910BAFC5AAD6B85F85796B42B
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD595CC96D701048F5A50ED9782C6AABF1C
PackageArchaarch64
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.
PackageMaintainerFedora Project
PackageNameshogun-doc-cn
PackageRelease0.33.git20141224.d71e19a.fc22
PackageVersion3.2.0.1
SHA-1102FFF431D96C919194DCBE874B432A65BA2A499
SHA-256721AACE41B35270AB9346DF56A18CF1BEE0A216D4C5FA93E791CF6A727BD99B7
Key Value
MD52EEC745F9CE248002037106425534DFF
PackageArchaarch64
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.
PackageMaintainerFedora Project
PackageNameshogun-doc-cn
PackageRelease0.33.git20141224.d71e19a.fc22
PackageVersion3.2.0.1
SHA-1D576A5B680253D2A8D20604CE15DB3372005A373
SHA-25667A9978CAFCCABF6932B986BC0F4197B4F946042519ECF629B7651D6AF5288BC