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.

This release contains several enhancements, cleanups and bugfixes:

Features

• Integrate LaRank.
• Memory Mapped Features (for data sets that don't fit into memory).
• Compressor module with compression and decompression support for lzo, gzip, bzip2 and lzma.
• Compressed String Features with on-the-fly decompression (CDecompressString preproc).
• Parallel computation of get_kernel_matrix().
• One may now prefix all shogun print/outputs with file name and line number (obj.io.enable_file_and_line())
• Chinese Documentation thanks Elpmis Lee.

Bugfixes

• Fix One class MKL testing in static interfaces.
• Configure fixes: Let octave not write history on configure; fail when cplex is forcefully enabled but not found; add cplex 12 support.
• Fix a problem with regression and CombinedKernels employing only Custom kernels.

Cleanup and API Changes

• String Features now (like SimpleFeatures) upon get_feature_vector require an additional do_free argument and need to be freed using free_feature_vector.

About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]

This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.

About: For modern biology, precise genome annotations are of prime importance as they allow the accurate definition of genic regions. We employ state of the art machine learning methods to assay and [...]

Initial Announcement on mloss.org.

About: Local alignment kernels measure the similarity between two sequences by summing up scores obtained from local alignments with gaps of the sequences.

Initial Announcement on mloss.org.