No edit summary |
No edit summary |
||
| Line 2: | Line 2: | ||
== Summary == | == Summary == | ||
The 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 and | |||
SVMlight. 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. | |||
SHOGUN is implemented in C++ and interfaces to C#, Java, Lua, | |||
Octave, Perl, Python, R and Ruby. | |||
== Owner == | == Owner == | ||
Revision as of 09:40, 23 September 2013
The Shogun Machine Learning Toolbox
Summary
The 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 and
SVMlight. 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.
SHOGUN is implemented in C++ and interfaces to C#, Java, Lua, Octave, Perl, Python, R and Ruby.
Owner
- Name: Björn Esser
- Email: besser82@fedoraproject.org
- Release notes owner:
Current status
- Targeted release: Fedora 21
- Last updated: 2013-09-23
- Tracker bug: <will be assigned by the Wrangler>
Detailed Description
Benefit to Fedora
Scope
- Proposal owners:
- Other developers: N/A (not a System Wide Change)
- Release engineering: N/A (not a System Wide Change)
- Policies and guidelines: N/A (not a System Wide Change)
Upgrade/compatibility impact
N/A (not a System Wide Change)
How To Test
N/A (not a System Wide Change)
User Experience
N/A (not a System Wide Change)
Dependencies
N/A (not a System Wide Change)
Contingency Plan
- Contingency mechanism: (What to do? Who will do it?) N/A (not a System Wide Change)
- Contingency deadline: N/A (not a System Wide Change)
- Blocks release? N/A (not a System Wide Change), Yes/No
Documentation
N/A (not a System Wide Change)