About: pycobra is a python library for ensemble learning, which serves as a toolkit for regression, classification, and visualisation. It is scikit-learn compatible and fits into the existing scikit-learn ecosystem.

pycobra is further pep8 compliant, has improved tests and more plotting options.

About: HLearn makes simple machine learning routines available in Haskell by expressing them according to their algebraic structure

Updated to version 1.0

About: This toolbox implements models for Bayesian mixed-effects inference on classification performance in hierarchical classification analyses.

In addition to the existing MATLAB implementation, the toolbox now also contains an R package of the variational Bayesian algorithm for mixed-effects inference.

About: Use the power of crowdsourcing to create ensembles.

Initial Announcement on mloss.org.

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: Itemset boosting (iBoost) performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation [...]

Initial Announcement on mloss.org.

About: PLearn is a large C++ machine-learning library with a set of Python tools and Python bindings. It is mostly a research platform for developing novel algorithms, and is being used extensively at [...]

Initial Announcement on mloss.org.