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Showing Items 191-200 of 588 on page 20 of 59: First Previous 15 16 17 18 19 20 21 22 23 24 25 Next Last

Logo ILNumerics.Net 1.4.01

by haymo - October 14, 2008, 01:24:28 CET [ Project Homepage BibTeX Download ] 5908 views, 1459 downloads, 1 subscription

About: Intended for .NET developers wanting to implement algorithms directly in a common .NET language (recommended: C#). Support for n-dim generic arrays, LAPACK, FFT, cells, logicals, 2D&3D plotting [...]

Changes:

Initial Announcement on mloss.org.


Logo dataformat 0.1.1

by mikio - March 12, 2009, 16:07:55 CET [ Project Homepage BibTeX Download ] 7644 views, 1456 downloads, 2 subscriptions

About: The goal of this project is to provide code for reading and writing machine learning data sets for as many programming languages as possible.

Changes:

Forgot to include the Java sources.


Logo OLL 0.02

by hillbig - May 21, 2009, 10:08:31 CET [ Project Homepage BibTeX Download ] 6651 views, 1455 downloads, 1 subscription

About: OLL is a library supporting several for online-learning algorithms, which provides C++ library, and stand-alone programs for learning, predicting. OLL is specialized for large-scale, but sparse, [...]

Changes:

Initial Announcement on mloss.org.


Logo r-cran-tree 1.0-29

by r-cran-robot - July 24, 2011, 00:00:00 CET [ Project Homepage BibTeX Download ] 7748 views, 1453 downloads, 1 subscription

About: Classification and regression trees

Changes:

Fetched by r-cran-robot on 2012-02-01 00:00:11.999664


Logo Somoclu 1.4.1

by peterwittek - January 28, 2015, 13:19:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7498 views, 1446 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. Apart from a command line interface, Python, R, and MATLAB are supported.

Changes:
  • Better support for ICC.
  • Faster code when compiling with GCC.
  • Building instructions and documentation improved.
  • Bug fixes: portability for R, using native R random number generator.

Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 7461 views, 1441 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

Changes:
  • 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 MPI IKL 1.0

by pgehler - January 16, 2009, 16:39:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7530 views, 1437 downloads, 1 subscription

About: This package contains an implementation of the Infinite Kernel Learning (IKL) algorithm and the SimpleMKL algorithm. This is realized by building on Coin-Ipopt-3.3.5 and Libsvm.

Changes:

Initial Announcement on mloss.org.


Logo Graph kernel based on iterative graph similarity and optimal assignments 2008-01-15

by mrupp - September 22, 2008, 13:42:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8386 views, 1431 downloads, 2 subscriptions

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About: Java package implementing a kernel for (molecular) graphs based on iterative graph similarity and optimal assignments.

Changes:

Initial Announcement on mloss.org.


Logo pSpectralClustering 1.1

by tbuehler - July 30, 2014, 19:44:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6360 views, 1427 downloads, 2 subscriptions

About: A generalized version of spectral clustering using the graph p-Laplacian.

Changes:
  • fixed compatibility issue with Matlab R2013a+
  • several internal optimizations

Logo VLFeat 0.9.16

by andreavedaldi - October 5, 2012, 18:44:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7757 views, 1427 downloads, 1 subscription

About: The VLFeat open source library implements popular computer vision algorithms including affine covariant feature detectors, HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.16.

Changes:

VLFeat 0.9.16: Added VL_COVDET() (covariant feature detectors). This function implements the following detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also implements affine adaptation, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet the auxiliary function VL_PLOTSS(). This is the second point update supported by the PASCAL Harvest programme.

VLFeat 0.9.15: Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a vastly improved SVM implementation. Added IHASHSUM (hashed counting). Improved INTHIST (integral histogram). Added VL_CUMMAX(). Improved the implementation of VL_ROC() and VL_PR(). Added VL_DET() (Detection Error Trade-off (DET) curves). Improved the verbosity control to AIB. Added support for Xcode 4.3, improved support for past and future Xcode versions. Completed the migration of the old test code in toolbox/test, moving the functionality to the new unit tests toolbox/xtest. Improved credits. This is the first point update supported by the PASCAL Harvest (several more to come shortly).


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