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 contains several enhancements, cleanups and bugfixes:
Features
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Integrate LaRank.
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Memory Mapped Features (for data sets that don't fit into memory).
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Compressor module with compression and decompression support for lzo, gzip, bzip2 and lzma.
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Compressed String Features with on-the-fly decompression (CDecompressString preproc).
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Parallel computation of get_kernel_matrix().
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One may now prefix all shogun print/outputs with file name and line number (obj.io.enable_file_and_line())
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Chinese Documentation thanks Elpmis Lee.
Bugfixes
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Fix One class MKL testing in static interfaces.
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Configure fixes: Let octave not write history on configure; fail when cplex is forcefully enabled but not found; add cplex 12 support.
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Fix a problem with regression and CombinedKernels employing only Custom kernels.
Cleanup and API Changes
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String Features now (like SimpleFeatures) upon get_feature_vector require an additional do_free argument and need to be freed using free_feature_vector.
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- Operating System:
Cygwin,
Linux,
Macosx
- Data Formats:
Plain Ascii,
Svmlight,
Fasta,
Fastq
- Tags:
Bioinformatics,
Large Scale,
String Kernel,
Kernel,
Kernelmachine,
Lda,
Lpm,
Matlab,
Mkl,
Octave,
Python,
R,
Svm
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