About: jblas is a fast linear algebra library for Java. jblas is based on BLAS and LAPACK, the de-facto industry standard for matrix computations, and uses state-of-the-art implementations like ATLAS for all its computational routines, making jBLAS very fast.

Changes from 1.0:

• Added singular value decomposition
• Fixed bug with returning complex values
• Many other minor improvements

About: redsvd is a library for solving several matrix decomposition (SVD, PCA, eigen value decomposition) redsvd can handle very large matrix efficiently, and optimized for a truncated SVD of sparse matrices. For example, redsvd can compute a truncated SVD with top 20 singular values for a 100K x 100K matrix with 10M nonzero entries in about two second.

Initial Announcement on mloss.org.

About: Pyriel is a Python system for learning classification rules from data. Unlike other rule learning systems, it is designed to learn rule lists that maximize the area under the ROC curve (AUC) instead of accuracy. Pyriel is mostly an experimental research tool, but it's robust and fast enough to be used for lightweight industrial data mining.

Various bug fixes.

About: Python Framework for Vector Space Modelling that can handle unlimited datasets (input can be streamed, algorithms work incrementally in constant memory).

• improved Latent Semantic Analysis (incremental SVD) performance: factorizing the English Wikipedia (3.1m documents, 400 factors) now takes 14h even in serial mode (i.e., on a single computer)
• several minor optimizations and bug fixes

About: The gmm toolbox contains code for density estimation using mixtures of Gaussians: Starting from simple kernel density estimation with spherical and diagonal Gaussian kernels over manifold Parzen window until mixtures of penalised full Gaussians with only a few components. The toolbox covers many Gaussian mixture model parametrisations from the recent literature. Most prominently, the package contains code to use the Gaussian Process Latent Variable Model for density estimation. Most of the code is written in Matlab 7.x including some MEX files.

Initial Announcement on mloss.org

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

xptopen.mex: [HTML_REMOVED] reads and writes SAS Transport format (*.xpt) ttest and ttest2: [HTML_REMOVED] paired and unpaired t-test for data with missing values (useful for users of Octave, and for Matlab users without statistics toolbox)

• train_sc: bug fix when labels are single column {-1,1} format
• test_sc: improved docu for R.output

For more details see: http://biosig-consulting.com/matlab/NaN/CHANGELOG

About: Tools to work on HDF5 files for mldata.org

• Added a fix when data.get_correct internally receives an array of array with values instead an array with values.
• Added support for sparse matrices in data.get_correct.

About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.

New matrix class Bugfixes More examples New penalty and potential functions Group sparsity

About: CARP: The Clustering Algorithms’ Referee Package

Options to add noise, outliers, inverse Box-Cox transformation.

About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...]

Update for v2.0