| Name : shogun-doc
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| Version : 4.0.1
| Vendor : Fedora Project
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| Release : 0.1.git20150808.779c3ad.fc23
| Date : 2015-08-09 01:58:31
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| Group : Unspecified
| Source RPM : shogun-4.0.1-0.1.git20150808.779c3ad.fc23.src.rpm
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| Size : 331.64 MB
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| Packager : Fedora Project
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| Summary : Documentation files for shogun
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Description :
Shogun\'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 OCAS,
Liblinear, LibSVM, SVMLin and GPDT.
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. For linear SVMs the COFFIN framework
allows for on-demand computing feature spaces on-the-fly, even allowing to
mix sparse, dense and other data types.
Furthermore, 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 one-class, 2-class and multiclass classification and
regression problems can be dealt with.
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 preprocessors (e.g.
substracting the mean) can be attached to each feature object allowing for
on-the-fly pre-processing.
SHOGUN is implemented in C++ with interfaces to R, Octave and Python and is
proudly released as Machine Learning Open Source Software.
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.
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RPM found in directory: /packages/linux-pbone/archive.fedoraproject.org/fedora/linux/releases/23/Everything/i386/os/Packages/s |
