Result for 007F0F181FEA3DEA64458BAE14F953DFE7115CFA

Query result

Key Value
FileName./usr/share/doc/shogun/html_cn/functions_x.html
FileSize8710
MD52631F4C49263253C5BE2AD632FB3D367
SHA-1007F0F181FEA3DEA64458BAE14F953DFE7115CFA
SHA-256323E35B3C8D8669D5243727E5A5B6454DAEAF5E6AA7B8F693AF3E9FE2E34D0E0
SSDEEP96:xBuTsRpRu9bL1somk5+pIDsgNZbTDQM5z5E5T5OBm45X5p5F85Ny5n5y5Z5s5b5y:3uTqpO/Essgl4N
TLSHT1C102F00524B30216427652DFADF7573874D381BAD3081E10B6AC999D6FDAF463C6B40F
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