< Changes
No edit summary |
|||
| Line 2: | Line 2: | ||
== Summary == | == Summary == | ||
The machine learning toolbox's focus is on large scale kernel | 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 | ||
methods and especially on Support Vector Machines (SVM). It | 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. | ||
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 | |||
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 | |||
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, | SHOGUN is implemented in C++ and interfaces to C#, Java, Lua, Octave, Perl, Python, R and Ruby. | ||
Octave, Perl, Python, R and Ruby. | |||
== Owner == | == Owner == | ||
Revision as of 09:42, 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)