About: A library of scalable Bayesian generalised linear models with fancy features

• 1.0 release!
• Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
• Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
• Random state can be set for all random objects
• Numerous small improvements to make revrand production ready
• Final report
• Documentation improvements

About: The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents.

• Experiments can now be invoked from the command line
• Experiments can now be "scripted"
• MMLF Experimenter contains now basic module for statistical hypothesis testing
• MMLF Explorer can now visualize the model that has been learned by an agent

About: Generalized linear and additive models by likelihood based boosting

Fetched by r-cran-robot on 2013-04-01 00:00:04.893311

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.

1.5 Changed CF (confidence factor) to do LaPlace smoothing of estimates. New flag "--score-for-class C" causes scores to be computed relative to a given (positive) class. For two-class problems. Fixed bug in example sampling code (--sample n) Fixed bug keeping old-style example formats (terminated by dot) from working. More code restructuring.

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: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.

JProGraM 13.2 -- CHANGE LOG

Release date: February 13, 2012

New features: -- Support for Fiedler random graphs/random field models for large-scale networks (ninofreno.graph.fiedler package); -- Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).

About: libnabo is a fast K Nearset Neighbor library for low-dimensional spaces.

• Reset point indices of results with distances exceeding threshold (#23, #24)
• Fine tune the find_package() capability and add uninstall target (#22)
• Fixed compiler warning (#18)
• Added OpenMP support (#20, #21)
• Build type tuning (#19)
• Fix: terminal comma in enum requires C++11
• Fix UBSAN error calculating maxNodeCount (#16, #17)
• Fixed tiny (yet significant) error in the Python doc strings (#15)
• Compile static lib with PIC (#14)
• Added configure scripts for full catkinization
• Catkinization of libnabo (following REP136)
• Update README.md Added Simon as the maintainer.
• [test] use CLOCK_PROF for NetBSD build
• Fixed CppCheck warning. Fix broken install when doxygen is not found
• Fix cmake stylistic issue
• Make python install respect custom CMAKE_INSTALL_PREFIX
• Fix broken install when doxygen is not found

About: Improved Predictors

Fetched by r-cran-robot on 2013-04-01 00:00:05.613011

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: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

• Add bibtex entry of corresponding Neurocomputing paper
• Create javascript modules to avoid global scope pollution in web pages

About: Open Source Machine Learning Server

• Single machine version for small-to-medium scale deployments
• Integrated GraphChi (disk-based large-scale graph computation) and algorithms: ALS, CCD++, SGD, CLiMF
• Improved runtime for training and offline evaluation
• Bug fixes

See release notes - https://predictionio.atlassian.net/secure/ReleaseNote.jspa?projectId=10000&version=11801

About: An open source Java software providing collaborative filtering algorithms.

Initial Announcement on mloss.org.

About: Toolbox for circular statistics with Matlab (The Mathworks).

Some bugfixes.

About: A Tool for Measuring String Similarity

This release fixes the incorrect implementation of the bag distance.

About: HSSVM is a software for solving multi-class problem using Hyper-sphere Support Vector Machines model, implemented by Java.

1. From this version, the version number is normalized to hssvm1.0.1;
2. In this version, we delete the features about running parameter searching and run-all from Ant script, that is, commands "ant search-param" and "ant run-all" which exist in previous version are no longer available, and they are replaced with commands "svm search conf" and "svm runall conf", both of them are used on Linux(or all other POSIX systems).If you want to use this program on Windows, the cygwin is required to be installed.

About: Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. We believe that Caffe is the fastest available GPU CNN implementation. Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters. Even in CPU mode, computing predictions on an image takes only 20 ms (in batch mode).

LOTS of stuff: https://github.com/BVLC/caffe/releases/tag/v0.9999