About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the wellknown stochastic algorithms for Machine Learning developed in the highlevel technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and nonlinear Support Vector Machines applied to large data samples with usercentric and userfriendly emphasis. Changes:Initial Announcement on mloss.org.

About: A platformindependent C++ framework for machine learning, graphical models, and computer vision research and development. Changes:Version 1.9:

About: A Machine Learning framework for ObjectiveC and Swift (OS X / iOS) Changes:Initial Announcement on mloss.org.

About: R package implementing statistical test and post hoc tests to compare multiple algorithms in multiple problems. Changes:Initial Announcement on mloss.org.

About: Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data. It leverages the scikitlearn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. Changes:Initial Announcement on mloss.org.

About: This Matlab package implements a method for learning a choquistic regression model (represented by a corresponding Moebius transform of the underlying fuzzy measure), using the maximum likelihood approach proposed in [2], eqquiped by sigmoid normalization, see [1]. Changes:Initial Announcement on mloss.org.

About: A Theano framework for building and training neural networks Changes:Initial Announcement on mloss.org.

About: A toolkit for hyperparameter optimization for machine learning algorithms. Changes:Initial Announcement on mloss.org.

About: MALSS is a python module to facilitate machine learning tasks. Changes:Initial Announcement on mloss.org.

About: Learns dynamic network changes across conditions and visualize the results in Cytoscape. Changes:Initial Announcement on mloss.org.

About: Hubnessaware Machine Learning for Highdimensional Data Changes:

About: C++ generic programming tools for machine learning Changes:Initial Announcement on mloss.org.

About: Java package for calculating Entropy for Machine Learning Applications. It has implemented several methods of handling missing values. So it can be used as a lab for examining missing values. Changes:Discretizing numerical values is added to calculate mode of values and fractional replacement of missing ones. class diagram is on the web http://profs.basu.ac.ir/bathaeian/free_space/jemla.rar

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over datadependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface. Changes:improved testing, improved documentation, windows compatibility, more algorithms

About: This package is an implementation of a linear RankSVM solver with nonconvex regularization. Changes:Initial Announcement on mloss.org.

About: Open Source Machine Learning Server Changes:
See release notes  https://predictionio.atlassian.net/secure/ReleaseNote.jspa?projectId=10000&version=11801

About: The package "fastclime" provides a method of recover the precision matrix efficiently by applying parametric simplex method. The computation is based on a linear optimization solver. It also contains a generic LP solver and a parameterized LP solver using parametric simplex method. Changes:Initial Announcement on mloss.org.

About: The package computes the optimal parameters for the Choquet kernel Changes:Initial Announcement on mloss.org.

About: Estimates statistical significance of association between variables and their principal components (PCs). Changes:Initial Announcement on mloss.org.

About: "Ordinal Choquistic Regression" model using the maximum likelihood Changes:Initial Announcement on mloss.org.
