9 projects found that use the lgpl 3 license.


Logo JMLR MLPACK 1.0.10

by rcurtin - August 29, 2014, 21:26:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 33443 views, 6645 downloads, 6 subscriptions

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About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • Bugfix for NeighborSearch regression which caused very slow allknn/allkfn. Speeds are nwo restored to approximately 1.0.8 speeds, with significant improvement for the cover tree (#365).
  • Detect dependencies correctly when ARMA_USE_WRAPPER is not defined (i.e. libarmadillo.so does not exist).
  • Bugfix for compilation under Visual Studio (#366).

Logo JMLR EnsembleSVM 2.0

by claesenm - March 31, 2014, 08:06:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5192 views, 1829 downloads, 2 subscriptions

About: The EnsembleSVM library offers functionality to perform ensemble learning using Support Vector Machine (SVM) base models. In particular, we offer routines for binary ensemble models using SVM base classifiers. Experimental results have shown the predictive performance to be comparable with standard SVM models but with drastically reduced training time. Ensemble learning with SVM models is particularly useful for semi-supervised tasks.

Changes:

The library has been updated and features a variety of new functionality as well as more efficient implementations of original features. The following key improvements have been made:

  1. Support for multithreading in training and prediction with ensemble models. Since both of these are embarassingly parallel, this has induced a significant speedup (3-fold on quad-core).
  2. Extensive programming framework for aggregation of base model predictions which allows highly efficient prototyping of new aggregation approaches. Additionally we provide several predefined strategies, including (weighted) majority voting, logistic regression and nonlinear SVMs of your choice -- be sure to check out the esvm-edit tool! The provided framework also allows you to efficiently program your own, novel aggregation schemes.
  3. Full code transition to C++11, the latest C++ standard, which enabled various performance improvements. The new release requires moderately recent compilers, such as gcc 4.7.2+ or clang 3.2+.
  4. Generic implementations of convenient facilities have been added, such as thread pools, deserialization factories and more.

The API and ABI have undergone significant changes, many of which are due to the transition to C++11.


Logo AIDE 0.2

by khalili - January 3, 2014, 18:01:06 CET [ Project Homepage BibTeX Download ] 971 views, 225 downloads, 1 subscription

About: AIDE (Automata Identification Engine) is a free open source tool for automata inference algorithms developed in C# .Net.

Changes:

Initial Announcement on mloss.org.


Logo Thalasso v0.2

by rherault - July 22, 2013, 15:33:59 CET [ Project Homepage BibTeX Download ] 922 views, 265 downloads, 1 subscription

About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2||X*beta-y||^2 + lambda*sum|beta_i|, where X and y are problem data and beta and lambda are variables.

Changes:

Initial Announcement on mloss.org.


Logo MLDemos 0.5.1

by basilio - March 2, 2013, 16:06:13 CET [ Project Homepage BibTeX Download ] 18421 views, 4375 downloads, 2 subscriptions

About: MLDemos is a user-friendly visualization interface for various machine learning algorithms for classification, regression, clustering, projection, dynamical systems, reward maximisation and reinforcement learning.

Changes:

New Visualization and Dataset Features Added 3D visualization of samples and classification, regression and maximization results Added Visualization panel with individual plots, correlations, density, etc. Added Editing tools to drag/magnet data, change class, increase or decrease dimensions of the dataset Added categorical dimensions (indexed dimensions with non-numerical values) Added Dataset Editing panel to swap, delete and rename dimensions, classes or categorical values Several bug-fixes for display, import/export of data, classification performance

New Algorithms and methodologies Added Projections to pre-process data (which can then be classified/regressed/clustered), with LDA, PCA, KernelPCA, ICA, CCA Added Grid-Search panel for batch-testing ranges of values for up to two parameters at a time Added One-vs-All multi-class classification for non-multi-class algorithms Trained models can now be kept and tested on new data (training on one dataset, testing on another) Added a dataset generator panel for standard toy datasets (e.g. swissroll, checkerboard,...) Added a number of clustering, regression and classification algorithms (FLAME, DBSCAN, LOWESS, CCA, KMEANS++, GP Classification, Random Forests) Added Save/Load Model option for GMMs and SVMs Added Growing Hierarchical Self Organizing Maps (original code by Michael Dittenbach) Added Automatic Relevance Determination for SVM with RBF kernel (Thanks to Ashwini Shukla!)


Logo TurboParser 2.0

by afm - October 11, 2012, 02:59:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4090 views, 903 downloads, 1 subscription

About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar.

Changes:

This version introduces a number of new features:

  • The parser does not depend anymore on CPLEX (or any other non-free LP solver). Instead, the decoder is now based on AD3, our free library for approximate MAP inference.

  • The parser now outputs dependency labels along with the backbone structure.

  • As a bonus, we now provide a trainable part-of-speech tagger, called TurboTagger, which can be used in standalone mode, or to provide part-of-speech tags as input for the parser. TurboTagger has state-of-the-art accuracy for English (97.3% on section 23 of the Penn Treebank) and is fast (~40,000 tokens per second).

  • The parser is much faster than in previous versions. You may choose among a basic arc-factored parser (~4,300 tokens per second), a standard second-order model with consecutive sibling and grandparent features (the default; ~1,200 tokens per second), and a full model with head bigram and arbitrary sibling features (~900 tokens per second).

Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package.

This software has been tested on Linux, but it should run in other platforms with minor adaptations.


Logo Nen Beta

by pascal - February 19, 2012, 00:31:34 CET [ Project Homepage BibTeX Download ] 3066 views, 916 downloads, 1 subscription

About: 3-layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM.

Changes:

Initial Announcement on mloss.org.


Logo QuickDT 0.1

by sanity - September 21, 2011, 13:43:37 CET [ Project Homepage BibTeX Download ] 2181 views, 661 downloads, 1 subscription

About: A decision tree learner that is designed to be reasonably fast, but the primary goal is ease of use

Changes:

Initial Announcement on mloss.org.


Logo seqan 1.2

by sonne - November 2, 2009, 14:54:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6120 views, 1181 downloads, 1 subscription

About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data.

Changes:
  • 5 more applications, i.e. DFI, MicroRazerS, PairAlign, SeqCons, TreeRecon
  • stable release of RazerS supporting paired-end read mapping and configurable sensitivity
  • new alignment algorithms, e.g. banded, configurable alignments (overlap, semi-global, ...)
  • realignment algorithm
  • NGS data structures and formats, e.g. SAM, Amos, ...
  • new alphabets, e.g. Dna with base call qualities, profile characters
  • auxiliary data structures and algorithms, e.g. double ended queue, command line parser
  • positional scores
  • CMake support