About: Nonnegative Sparse Coding, Discriminative Semi-supervised Learning, sparse probability graph

Initial Announcement on mloss.org.

About: This MATLAB package provides the MLAPG algorithm proposed in our ICCV 2015 paper. It is efficient for PSD constrained metric learning, and also effective for person re-identification. For more details, please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/mlapg/.

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: ELF provides many well implemented supervised learners for classification and regression tasks with an opportunity of ensemble learning.

Initial Announcement on mloss.org.

About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.

Additions and improvements from ELKI 0.7.0 to 0.7.1:

Algorithm additions:

• GriDBSCAN: DBSCAN using grid partitioning (Minkowski distances only)

• Compare-Means and Sort-Means k-means variations (much faster than traditional k-means)

• Visualization of dendrograms.

Important bug fixes:

• Classes with no package ("default package") would cause errors.

• The fast power function implementation was sometimes returning incorrect results.

• Random sampling was sometimes not sampling from the full data set.

UI improvements:

• The file input source will now automatically choose the Arff parser for .arff files.

• MiniGUI now allows choosing other applications.

• MiniGUI now displays the command line in a separate field.

• MiniGUI displays an error message, if an incorrect classpath or JAyatana (on Ubuntu) is detected.

• Export to png now works, we added a work-around for an open Batik bug.

Smaller changes:

• Many smaller bug fixes.

• C-Index for cluster evaluation now can process larger data sets.

• OPTICS output of undefined reachability fixed.

• External distance matrixes are easier to use and perform additional checks.

• Precomputed distance matrixes can answer range and kNN queries.

• Voronoi visualization can be switched in the menu now.

• Improved backwards command line compatibility with additional aliases.

• Added generated @since annotations in JavaDoc.

• Many new unit tests, renamed to the Java conventions.

• Low-level reading of service files, to have faster startup.

About: Encog is a Machine Learning framework for Java, C#, Javascript and C/C++ that supports SVM's, Genetic Programming, Bayesian Networks, Hidden Markov Models and other algorithms.

Changes for Encog 3.2:

Issue #53: Fix Out Of Range Bug In BasicMLSequenceSet. Issue #52: Unhandled exception in Encog.Util.File.ResourceLoader.CreateStream (ResourceLoader.cs) Issue #50: Concurrency bugs in PruneIncremental Issue #48: Unit Tests Failing - TestHessian Issue #46: Couple of small fixes - Temporal DataSet and SCG training Issue #45: Fixed EndMinutesStrategy to correctly evaluate ShouldStop after the specified number of minutes have elapsed. Issue #44: Encog.ML.Data.Basic.BasicMLDataPairCentroid.Add() & .Remove() Issue #43: Unit Tests Failing - Matrix not full rank Issue #42: Nuget - NuSpec Issue #36: Load Examples easier

About: Embarrassingly Parallel Array Computing: EPAC is a machine learning workflow builder.

Initial Announcement on mloss.org.

About: Epistatic miniarray profiles (E-MAPs) are a high-throughput approach capable of quantifying aggravating or alleviating genetic interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the effectiveness of some data analysis techniques and prevent the use of others. This project contains nearest neighbor based tools for the imputation and prediction of these missing values. The code is implemented in Python and uses a nearest neighbor based approach. Two variants are used - a simple weighted nearest neighbors, and a local least squares based regression.

Initial Announcement on mloss.org.

About: This evaluation toolkit provides a unified framework for evaluating bag-of-words based encoding methods over several standard image classification datasets.

Initial Announcement on mloss.org.

About: This page contains the implementation used in the paper „Experimental Design for Efficient Identification of Gene Regulatory Networks using Sparse Bayesian Models“ by Florian Steinke, Matthias [...]

Initial Announcement on mloss.org.

About: Experiment Databases for Machine Learning is a large public database of machine learning experiments as well as a framework for producing similar databases for specific goals. It provides a way to [...]

Initial Announcement on mloss.org.

About: Scalable tensor factorization

• Improve (speed up) initialization of A by summation

About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing.

CHANGES IN VERSION 2.8.0

NEW FEATURES

o rescaling of lapla
o extractPlot does not plot sorted matrices

CHANGES IN VERSION 2.4.0

o spfabia bugfixes

CHANGES IN VERSION 2.3.1

NEW FEATURES

o Getters and setters for class Factorization

2.0.0:

• spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
• fabia for non-negative factorization (parameter: non_negative);
• changed to C and removed dependencies to Rcpp;
• improved update for lambda (alpha should be smaller, e.g. 0.03);
• introduced maximal number of row elements (lL);
• introduced cycle bL when upper bounds nL or lL are effective;
• reduced computational complexity;
• bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;

1.4.0:

• New option nL: maximal number of biclusters per row element;
• Sort biclusters according to information content;
• Improved and extended preprocessing;
• Update to R2.13

About: [FACTORIE](http://factorie.cs.umass.edu) is a toolkit for deployable probabilistic modeling, implemented as a software library in [Scala](http://scala-lang.org). It provides its users with a succinct language for creating [factor graphs](http://en.wikipedia.org/wiki/Factor_graph), estimating parameters and performing inference. It also has implementations of many machine learning tools and a full NLP pipeline.

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.

Initial Announcement on mloss.org.

About: The open-source C-package fastICA implements the fastICA algorithm of Aapo Hyvarinen et al. (URL: http://www.cs.helsinki.fi/u/ahyvarin/) to perform Independent Component Analysis (ICA) and Projection Pursuit. fastICA is released under the GNU Public License (GPL).

Initial Announcement on mloss.org.