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About: The Chestnut Machine Learning Library is a suite of machine learning algorithms written in Python with some code written in C for efficiency. Most algorithms are called with a simple, functional API [...] Changes:Initial Announcement on mloss.org.

About: Source code for EM approximate learning in the Latent Topic Hypertext Model. Changes:Initial Announcement on mloss.org.

About: Dataefficient policy search framework using probabilistic Gaussian process models Changes:Initial Announcement on mloss.org.

About: A comprehensive data mining environment, with a variety of machine learning components. Changes:Modifications following feedback from Knime main Author.

About: A library for artificial neural networks. Changes:Added algorithms:

About: 3layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM. Changes:Initial Announcement on mloss.org.

About: This package is a set of Matlab scripts that implements the algorithms described in the submitted paper: "LpLq Sparse Linear and Sparse Multiple Kernel MultiTask Learning". Changes:Initial Announcement on mloss.org.

About: HLearn makes simple machine learning routines available in Haskell by expressing them according to their algebraic structure Changes:Updated to version 1.0

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: Multiclass vector classification based on cost functiondriven learning vector quantization , minimizing misclassification. Changes:Initial Announcement on mloss.org.
