About:
The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line.
Changes:
This release also contains several enhancements, cleanups and bugfixes:
Features
-
Linear Time MMD two-sample test now works on streaming-features, which
allows to perform tests on infinite amounts of data. A block size may
be specified for fast processing. The below features were also added.
By Heiko Strathmann.
-
It is now possible to ask streaming features to produce an instance
of streamed features that are stored in memory and returned as a
CFeatures* object of corresponding type. See
CStreamingFeatures::get_streamed_features().
-
New concept of artificial data generator classes: Based on streaming
features. First implemented instances are CMeanShiftDataGenerator and
CGaussianBlobsDataGenerator.
Use above new concepts to get non-streaming data if desired.
-
Accelerated projected gradient multiclass logistic regression classifier
by Sergey Lisitsyn.
-
New CCSOSVM based structured output solver by Viktor Gal
-
A collection of kernel selection methods for MMD-based kernel two-
sample tests, including optimal kernel choice for single and combined
kernels for the linear time MMD. This finishes the kernel MMD framework
and also comes with new, more illustrative examples and tests.
By Heiko Strathmann.
-
Alpha version of Perl modular interface developed by Christian Montanari.
-
New framework for unit-tests based on googletest and googlemock by
Viktor Gal. A (growing) number of unit-tests from now on ensures basic
funcionality of our framework. Since the examples do not have to take
this role anymore, they should become more ilustrative in the future.
-
Changed the core of dimension reduction algorithms to the Tapkee library.
Bugfixes
-
Fix for shallow copy of gaussian kernel by Matt Aasted.
-
Fixed a bug when using StringFeatures along with kernel machines in
cross-validation which cause an assertion error. Thanks to Eric (yoo)!
-
Fix for 3-class case training of MulticlassLibSVM reported by Arya Iranmehr
that was suggested by Oksana Bayda.
-
Fix for wrong Spectrum mismatch RBF construction in static interfaces reported
by Nona Kermani.
-
Fix for wrong include in SGMatrix causing build fail on Mac OS X
(thanks to @bianjiang).
-
Fixed a bug that caused kernel machines to return non-sense when using
custom kernel matrices with subsets attached to them.
-
Fix for parameter dictionary creationg causing dereferencing null pointers
with gaussian processes parameter selection.
-
Fixed a bug in exact GP regression that caused wrong results.
-
Fixed a bug in exact GP regression that produced memory errors/crashes.
-
Fix for a bug with static interfaces causing all outputs to be
-1/+1 instead of real scores (reported by Kamikawa Masahisa).
Cleanup and API Changes
-
SGStringList is now based on SGReferencedData.
-
"confidences" in context of CLabel and subclasses are now "values".
-
CLinearTimeMMD constructor changes, only streaming features allowed.
-
CDataGenerator will soon be removed and replaced by new streaming-
based classes.
-
SGVector, SGMatrix, SGSparseVector, SGSparseVector, SGSparseMatrix
refactoring: Now contains load/save routines, relevant functions from
CMath, and implementations went to .cpp file.
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- Operating System:
Cygwin,
Linux,
Macosx,
Bsd
- Data Formats:
Plain Ascii,
Svmlight,
Binary,
Fasta,
Fastq,
Hdf
- JMLR-MLOSS Publication:
JMLR Page
- Tags:
Bioinformatics,
Large Scale,
String Kernel,
Kernel,
Kernelmachine,
Lda,
Lpm,
Matlab,
Mkl,
Octave,
Python,
R,
Svm,
Sgd,
Icml2010,
Liblinear,
Libsvm,
Multiple Kernel Learning,
Ocas,
Gaussian Processes,
Reg
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