20 projects found that use c++ as the programming language.
Showing Items 1-20 of 175 on page 1 of 9: 1 2 3 4 5 6 Next Last

Logo JMLR dlib ml 18.12

by davis685 - December 20, 2014, 22:38:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 87721 views, 15181 downloads, 2 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release adds tools for computing 2D FFTs, Hough transforms, image skeletonizations, and also a simple and type safe API for calling C++ code from MATLAB.


Logo JMLR MLPACK 1.0.11

by rcurtin - December 11, 2014, 18:20:35 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 35767 views, 7011 downloads, 6 subscriptions

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

Changes:
  • Proper handling of dimension calculation in PCA.
  • Load parameter vectors properly for LinearRegression models.
  • Linker fixes for AugLagrangian specializations under Visual Studio.
  • Add support for observation weights to LinearRegression.
  • MahalanobisDistance<> now takes root of the distance by default and therefore satisfies the triangle inequality (TakeRoot now defaults to true).
  • Better handling of optional Armadillo HDF5 dependency.
  • Fixes for numerous intermittent test failures.
  • math::RandomSeed() now sets the seed for recent (>= 3.930) Armadillo versions.
  • Handle Newton method convergence better for SparseCoding::OptimizeDictionary() and make maximum iterations a parameter.
  • Known bug: CosineTree construction may fail in some cases on i386 systems (376).

Logo APCluster 1.4.1

by UBod - December 10, 2014, 12:58:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18452 views, 3385 downloads, 3 subscriptions

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About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplar-based agglomerative clustering, and various tools for visual analysis of clustering results.

Changes:
  • fixes in C++ code of sparse affinity propagation

Logo libAGF 0.9.8

by Petey - December 6, 2014, 02:35:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9267 views, 1850 downloads, 2 subscriptions

About: C++ software for statistical classification, probability estimation and interpolation/non-linear regression using variable bandwidth kernel estimation.

Changes:

New in Version 0.9.8:

  • bug fixes: svm file conversion works properly and is more general

  • non-hierarchical multi-borders has 3 options for solving for the conditional probabilities: matrix inversion, voting, and matrix inversion over-ridden by voting, with re-normalization

  • multi-borders now works with external binary classifiers

  • random numbers resolve a tie when selecting classes based on probabilities

  • pair of routines, sort_discrete_vectors and search_discrete_vectors, for classification based on n-d binning (still experimental)

  • command options have been changed with many new additions, see QUICKSTART file or run the relevant commands for details


Logo The Statistical ToolKit 0.8.4

by joblion - December 5, 2014, 13:21:47 CET [ Project Homepage BibTeX Download ] 667 views, 193 downloads, 2 subscriptions

About: STK++: A Statistical Toolkit Framework in C++

Changes:

Inegrating openmp to the current release. Many enhancement in the clustering project. bug fix


About: a parallel LDA learning toolbox in Multi-Core Systems for big topic modeling.

Changes:

Initial Announcement on mloss.org.


Logo Armadillo library 4.550

by cu24gjf - December 5, 2014, 03:24:54 CET [ Project Homepage BibTeX Download ] 48028 views, 10314 downloads, 4 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).

Changes:
  • added matrix exponential function: expmat()
  • faster .log_p() and .avg_log_p() functions in the Gaussian mixture model class
  • faster handling of in-place addition/subtraction of expressions with an outer product
  • workaround for a bug in GCC 4.4

Logo KeBABS 1.0.2

by UBod - December 4, 2014, 09:15:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1009 views, 147 downloads, 1 subscription

About: Kernel-Based Analysis Of Biological Sequences

Changes:
  • a few C code changes for mismatch kernel
  • correction of MCC
  • correction of computation of feature weights for LiblineaR with more than 3 classes

Logo OpenNN 1.0

by Sergiointelnics - November 21, 2014, 13:15:47 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 642 views, 109 downloads, 3 subscriptions

About: OpenNN is an open source class library written in C++ which implements neural networks. The library has been designed to learn from both data sets and mathematical models.

Changes:

Initial Announcement on mloss.org.


Logo libcmaes 0.9.3

by beniz - November 17, 2014, 14:04:10 CET [ Project Homepage BibTeX Download ] 2830 views, 592 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.

Changes:

This is an important update:

  • full support for surrogates, allowing optimization of costly objective functions, ref #57

  • integrated rankign SVM default surrogate, ref #83

  • Python bindings for surrogates, ref #75

  • more informed optimization status and error messages, ref #85

  • API for computing confidence intervals around optima, ref #30

  • API for computing 2D contour around optima, ref #31

  • new 'elitist' scheme for improved restart strategy useful on some rather difficult functions, ref #77

  • fixed Eigen namespace import, ref #62

  • fixed and added new parameter vector getter in Candidate, ref #84


Logo libcluster 2.1

by dsteinberg - October 31, 2014, 23:27:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 490 views, 93 downloads, 2 subscriptions

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

Changes:

Initial Announcement on mloss.org.


Logo BayesOpt, a Bayesian Optimization toolbox 0.7.2

by rmcantin - October 10, 2014, 19:12:59 CET [ Project Homepage BibTeX Download ] 9850 views, 2020 downloads, 4 subscriptions

About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python.

Changes:

-Fixed bugs and doc typos


Logo Somoclu 1.4

by peterwittek - September 5, 2014, 13:01:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5185 views, 982 downloads, 2 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes.

Changes:
  • Better Windows support.
  • Completed CUDA support for Python and R interfaces.
  • Faster compilation by removing unnecessary flags for nvcc
  • Support for CUDA 6.5.
  • Bug fixes: R version no longer needs separate code.

Logo JMLR Darwin 1.8

by sgould - September 3, 2014, 08:42:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 30790 views, 6453 downloads, 4 subscriptions

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

Changes:

Version 1.8:

  • Added Superpixel Graph Label Transfer (nnGraph) Project project
  • Added Python scripts for automating some projects
  • Added ability to pre-process features on-the-fly with one drwnFeatureTransform when applying or learning another drwnFeatureTransform
  • Fixed race condition in Windows threading (thanks to Edison Guo)
  • Switched Windows and Linux to build against OpenCV 2.4.9
  • Changed drwnMAPInference::inference to return upper and lower energy bounds
  • Added pruneRounds function to drwnBoostedClassifier
  • Added drwnSLICSuperpixels function
  • Added drwnIndexQueue class
  • mexLearnClassifier and mexAnalyseClassifier now support integer label types
  • Bug fix in mexSaveSuperpixels to support single channel

Logo XGBoost v0.3.0

by crowwork - September 2, 2014, 02:43:31 CET [ Project Homepage BibTeX Download ] 3312 views, 632 downloads, 2 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.

Changes:

New features: - R support that is now on CRAN

  • Faster tree construction module

  • Support for boosting from initial predictions

  • Linear booster is now parallelized, using parallel coordinated descent.


Logo RLPy 1.3a

by bobklein2 - August 28, 2014, 14:34:35 CET [ Project Homepage BibTeX Download ] 2114 views, 474 downloads, 1 subscription

About: RLPy is a framework for performing reinforcement learning (RL) experiments in Python. RLPy provides a large library of agent and domain components, and a suite of tools to aid in experiments (parallelization, hyperparameter optimization, code profiling, and plotting).

Changes:
  • Fixed bug where results using same random seed were different with visualization turned on/off
  • Created RLPy package on pypi (Available at https://pypi.python.org/pypi/rlpy)
  • Switched from custom logger class to python default
  • Added unit tests
  • Code readability improvements (formatting, variable names/ordering)
  • Restructured TD Learning heirarchy
  • Updated tutorials

Logo CURFIL 1.1

by hanschul - August 18, 2014, 13:54:31 CET [ Project Homepage BibTeX Download ] 670 views, 155 downloads, 1 subscription

About: CURFIL uses NVIDIA CUDA to accelerate random forest training and prediction for RGB and RGB-D images. It focuses on image labelling tasks, such as image segmentation or classification applications. CURFIL allows to search for optimal hyper-parameter configurations (e.g. using the hyperopt) package) by massively decreasing training time.

Changes:

Initial Announcement on mloss.org.


Logo Caffe 0.9999

by sergeyk - August 9, 2014, 01:57:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4538 views, 784 downloads, 2 subscriptions

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).

Changes:

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


Logo Boosted Decision Trees and Lists 1.0.4

by melamed - July 25, 2014, 23:08:32 CET [ BibTeX Download ] 3177 views, 953 downloads, 3 subscriptions

About: Boosting algorithms for classification and regression, with many variations. Features include: Scalable and robust; Easily customizable loss functions; One-shot training for an entire regularization path; Continuous checkpointing; much more

Changes:
  • added ElasticNets as a regularization option
  • fixed some segfaults, memory leaks, and out-of-range errors, which were creeping in in some corner cases
  • added a couple of I/O optimizations

Logo JMLR Waffles 2014-07-05

by mgashler - July 20, 2014, 04:53:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 25294 views, 7393 downloads, 2 subscriptions

About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.)

Changes:

Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html


Showing Items 1-20 of 175 on page 1 of 9: 1 2 3 4 5 6 Next Last