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Logo Armadillo library 6.100

by cu24gjf - October 3, 2015, 07:12:38 CET [ Project Homepage BibTeX Download ] 64181 views, 13039 downloads, 5 subscriptions

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About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use, with a function syntax similar to MATLAB. Matrix decompositions are provided through optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL, OpenBLAS).

  • faster norm() and normalise() when using Intel MKL, ATLAS or OpenBLAS
  • faster handling of compound expressions by join_rows() and join_cols()
  • added Schur decomposition: schur()
  • added .each_slice() for repeated matrix operations on each slice of a cube
  • expanded join_slices() to handle joining cubes with matrices
  • expanded .each_col() and .each_row() to handle out-of-place operations
  • stricter handling of matrix objects by hist() and histc()
  • Cube class now delays allocation of .slice() related structures until needed

Logo MLweb 0.1.1

by lauerfab - September 22, 2015, 09:57:44 CET [ Project Homepage BibTeX Download ] 626 views, 160 downloads, 2 subscriptions

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.

  • Smaller source package
  • Fix Makefile
  • Fix MathJax path

Logo JMLR Darwin 1.9

by sgould - September 8, 2015, 06:50:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 39740 views, 8232 downloads, 4 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.


Version 1.9:

  • Replaced drwnInPaint class with drwnImageInPainter class and added inPaint application
  • Added function to read CIFAR-10 and CIFAR-100 style datasets (see
  • Added drwnMaskedPatchMatch, drwnBasicPatchMatch, drwnSelfPatchMatch and basicPatchMatch application
  • drwnPatchMatchGraph now allows multiple matches to the same image
  • Upgraded wxWidgets to 3.0.2 (problems on Mac OS X)
  • Switched Mac OS X compilation to libc++ instead of libstdc++
  • Added Python scripts for running experiments and regression tests
  • Refactored drwnGrabCutInstance class to support both GMM and colour histogram model
  • Added cacheSortIndex to drwnDecisionTree for trading-off speed versus memory usage
  • Added mexLoadPatchMatchGraph for loading drwnPatchMatchGraph objects into Matlab
  • Improved documentation, other bug fixes and performance improvements

Logo PyScriptClassifier 0.0.1

by cjb60 - August 15, 2015, 05:14:59 CET [ Project Homepage BibTeX Download ] 518 views, 143 downloads, 1 subscription

About: Easily prototype WEKA classifiers using Python scripts.


Initial Announcement on

Logo Libra 1.1.2c

by lowd - June 25, 2015, 00:10:25 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15478 views, 3388 downloads, 3 subscriptions

About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, arithmetic circuits, and mixtures of trees.


Version 1.1.2c (6/24/2015):

  • Libra can now be installed via OPAM as well. To install OPAM, see: . Then run: "opam install libra-tk".
  • Updated documentation to describe OPAM installation.

Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 9070 views, 1776 downloads, 3 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

  • Distributed version of xgboost that runs on YARN, scales to billions of examples

  • Direct save/load data and model from/to S3 and HDFS

  • Feature importance visualization in R module, by Michael Benesty

  • Predict leaf index

  • Poisson regression for counts data

  • Early stopping option in training

  • Native save load support in R and python

  • xgboost models now can be saved using save/load in R

  • xgboost python model is now pickable

  • sklearn wrapper is supported in python module

  • Experimental External memory version

Logo Harry 0.4.0

by konrad - March 30, 2015, 14:03:12 CET [ Project Homepage BibTeX Download ] 5192 views, 1125 downloads, 2 subscriptions

About: A Tool for Measuring String Similarity


The new release supports measuring string similarity at the granularity of bytes, bits and tokens. A Python interface has been added. Several minor bugs have been fixed.

Logo JMLR Sally 1.0.0

by konrad - March 26, 2015, 17:01:35 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 30118 views, 5885 downloads, 3 subscriptions

About: A Tool for Embedding Strings in Vector Spaces


Support for explicit selection of granularity added. Several minor bug fixes. We have reached 1.0

Logo libcmaes 0.9.5

by beniz - March 9, 2015, 09:05:22 CET [ Project Homepage BibTeX Download ] 6365 views, 1300 downloads, 3 subscriptions

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.


This is a major release, with several novelties, improvements and fixes, among which:

  • step-size two-point adaptaion scheme for improved performances in some settings, ref #88

  • important bug fixes to the ACM surrogate scheme, ref #57, #106

  • simple high-level workflow under Python, ref #116

  • improved performances in high dimensions, ref #97

  • improved profile likelihood and contour computations, including under geno/pheno transforms, ref #30, #31, #48

  • elitist mechanism for forcing best solutions during evolution, ref 103

  • new legacy plotting function, ref #110

  • optional initial function value, ref #100

  • improved C++ API, ref #89

  • Python bindings support with Anaconda, ref #111

  • configure script now tries to detect numpy when building Python bindings, ref #113

  • Python bindings now have embedded documentation, ref #114

  • support for Travis continuous integration, ref #122

  • lower resolution random seed initialization

Logo JMLR RL library 3.00.00

by frezza - January 13, 2015, 04:15:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1742 views, 440 downloads, 2 subscriptions

About: A template based C++ reinforcement learning library


Initial Announcement on

Logo linearizedGP 1.0

by dsteinberg - November 28, 2014, 07:02:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1176 views, 288 downloads, 1 subscription

About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.


Initial Announcement on

Logo Lua MapReduce v0.3.6

by pakozm - November 15, 2014, 13:20:01 CET [ Project Homepage BibTeX Download ] 3784 views, 906 downloads, 3 subscriptions

About: Lua-MapReduce framework implemented in Lua using luamongo driver and MongoDB as storage. It follows Iterative MapReduce for training of Machine Learning statistical models.

  • Improved tuple implementation.

Logo pySPACE 1.2

by krell84 - October 29, 2014, 15:36:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3753 views, 809 downloads, 1 subscription

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 data-dependent 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.


improved testing, improved documentation, windows compatibility, more algorithms

Logo Salad 0.5.0

by chwress - August 22, 2014, 17:54:56 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6881 views, 1276 downloads, 1 subscription

About: A Content Anomaly Detector based on n-Grams


Lots and lots of cool new features and bugfixes ;)

  • Refinements to the user interface: This includes a progress indicator, colors, etc.
  • Determine the expected error (salad-inspect)
  • Enable the user to echo the used parametrization: salad [train|predict|inspect] --echo-params
  • Allow to set the input batch size as program argument: salad [train|predict|inspect] --batch-size
  • libsalad: The library allows to access salad's basic functions
  • Installers and precompiled binaries: Windows installer, Debian (ppa:chwress/salad) & RPM packages as well a generic linux installers.
  • Various minor bug fixes
  • Support for "length at end" zip files
  • Improve salad's usage in a 2-class setting: salad [train|predict|inspect] --input-filter

Logo Encog Machine Learning Framework 3.2

by jeffheaton - July 5, 2014, 23:47:06 CET [ Project Homepage BibTeX Download ] 4795 views, 1898 downloads, 1 subscription

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

Logo A Pattern Recognizer In Lua with ANNs v0.3.1

by pakozm - May 30, 2014, 10:49:10 CET [ Project Homepage BibTeX Download ] 4708 views, 1145 downloads, 2 subscriptions

About: APRIL-ANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising auto-encoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others.

  • Removed bugs.
  • Added Travis CI support.
  • KNN and clustering algorithms.
  • ZCA and PCA whitening.
  • Quickprop and ASGD optimization algorithms.
  • QLearning trainer.
  • Sparse float matrices are available in CSC an CSR formats.
  • Compilation with Homebrew and MacPorts available.
  • Compilation issues in Ubuntu 12.04 solved.

Logo libstb 1.8

by wbuntine - April 24, 2014, 09:02:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7427 views, 1457 downloads, 1 subscription

About: Generalised Stirling Numbers for Pitman-Yor Processes: this library provides ways of computing generalised 2nd-order Stirling numbers for Pitman-Yor and Dirichlet processes. Included is a tester and parameter optimiser. This accompanies Buntine and Hutter's article:, and a series of papers by Buntine and students at NICTA and ANU.


Moved repository to GitHub, and added thread support to use the main table lookups in multi-threaded code.

Logo MShadow 1.0

by antinucleon - April 10, 2014, 02:57:54 CET [ Project Homepage BibTeX Download ] 1566 views, 435 downloads, 1 subscription

About: Lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. Support element-wise expression expand in high performance. Code once, run smoothly on both GPU and CPU


Initial Announcement on

Logo CXXNET 0.1

by antinucleon - April 10, 2014, 02:47:08 CET [ Project Homepage BibTeX Download ] 2101 views, 459 downloads, 1 subscription

About: CXXNET (spelled as: C plus plus net) is a neural network toolkit build on mshadow( It is yet another implementation of (convolutional) neural network. It is in C++, with about 1000 lines of network layer implementations, easily configuration via config file, and can get the state of art performance.


Initial Announcement on

Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 30861 views, 5250 downloads, 1 subscription

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.


Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).

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