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: FAST is an implementation of Hidden Markov Models with Features. It allows features to modify both emissions and transition probabilities. Changes:Initial Announcement on mloss.org.

About: C5.0 Decision Trees and RuleBased Models Changes:Fetched by rcranrobot on 20170401 00:00:03.975157

About: rabit (Reliable Allreduce and Broadcast Interface) is a light weight library that provides a fault tolerant interface of Allreduce and Broadcast for portable , scalable and reliable distributed machine learning programs. Rabit programs can run on various platforms such as Hadoop, MPI and no installation is needed. Rabit now support kmeans clustering, and distributed xgboost: an extremely efficient disrtibuted boosted tree(GBDT) toolkit. Changes:Initial Announcement on mloss.org.

About: C++ software for statistical classification, probability estimation and interpolation/nonlinear regression using variable bandwidth kernel estimation. Changes:New in Version 0.9.8:

About: A MATLAB toolbox for defining complex machine learning comparisons Changes:Initial Announcement on mloss.org.

About: A community detection method based on constrained fractional set programming (CFSP). Changes:Initial Announcement on mloss.org.

About: Generalized Ridge Regression (with special advantage for p >> n cases) Changes:Fetched by rcranrobot on 20170401 00:00:03.608122

About: A generalized version of spectral clustering using the graph pLaplacian. Changes:

About: Scriptfriendly commandline tools for machine learning and data mining tasks. (The commandline tools wrap functionality from a public domain C++ class library.) Changes:Added support for CUDA GPUparallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html

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

About: PyStruct is a framework for learning structured prediction in Python. It has a modular interface, similar to the wellknown SVMstruct. Apart from learning algorithms it also contains model formulations for popular CRFs and interfaces to many inference algorithm implementation. Changes:Initial Announcement on mloss.org.

About: Efficient implementation of SemiStochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2regularized). Changes:Initial Announcement on mloss.org.

About: This package implements Ideal PCA in MATLAB. Ideal PCA is a (cross)kernel based feature extraction algorithm which is (a) a faster alternative to kernel PCA and (b) a method to learn data manifold certifying features. Changes:Initial Announcement on mloss.org.

About: Document/Text preprocessing for topic models: suite of Perl scripts for preprocessing text collections to create dictionaries and bag/list files for use by topic modelling software. Changes:Moved distribution and code across to GitHub. Changed "ldac" format to have 0 offset for word indices. Added "document frequency" (df) filtering on selection of tokens for linkTables. Playing with linkParse but its still unuseable generally.

About: Big Random Forests Changes:Fetched by rcranrobot on 20151101 00:00:04.072762

About: SAMOA is a platform for mining big data streams. It is a distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms. Changes:Initial Announcement on mloss.org.

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/lowrank learning/reconstruction based on the dual augmented Lagrangian method. Changes:

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

About: DRVQ is a C++ library implementation of dimensionalityrecursive vector quantization, a fast vector quantization method in highdimensional Euclidean spaces under arbitrary data distributions. It is an approximation of kmeans that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a byproduct of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. Changes:Initial Announcement on mloss.org.
