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Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 7953 views, 1570 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 Debellor 1.0

by mwojnars - July 30, 2009, 16:48:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7949 views, 2237 downloads, 1 subscription

About: Debellor is a scalable and extensible platform which provides common architecture for data mining and machine learning algorithms of various types.

  • Naming of numerous classes/methods/fields changed to be more accurate and comprehensible
  • Weka and Rseslib libraries updated to the newest versions: Weka 3.6.1 & Rseslib 3.0.1. Debellor's wrappers adapted
  • New class: CrossValidation - evaluator of trainable cells through cross-validation
  • New class: RMSE - calculation of Root Mean Squared Error score
  • Data objects can be compared and used in collections
  • ArffReader can read from a user-provided
  • More convenient use of parameters (setting values)
  • More convenient use of data objects and data types (construction, type casting)
  • Other minor improvements to existing classes
  • Javadoc extended

Logo JMLR CAM Java 3.1

by wangny - October 14, 2013, 22:46:03 CET [ Project Homepage BibTeX Download ] 7932 views, 3368 downloads, 1 subscription

About: The CAM R-Java software provides a noval way to solve blind source separation problem.


In this version, we fix the problem of not working under newest R version R-3.0.

Logo VLFeat 0.9.16

by andreavedaldi - October 5, 2012, 18:44:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7893 views, 1459 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.


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).

Logo pGBRT, Parallel Gradient Boosted Regression Trees 0.9

by swtyree - September 16, 2011, 22:15:46 CET [ Project Homepage BibTeX Download ] 7890 views, 1259 downloads, 1 subscription

About: Learns gradient boosted regression tree ensembles in parallel on shared memory or cluster systems


Initial Announcement on

Logo DeltaLDA 0.1.1

by davidandrzej - July 16, 2009, 21:52:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7869 views, 1393 downloads, 1 subscription

About: This software implements the DeltaLDA model, which is a modification of the Latent Dirichlet Allocation (LDA) model. DeltaLDA can use multiple topic mixing weight priors to jointly model multiple [...]


-fixed some npy_intp[] memory leaks -fixed phi normalization bug

Logo r-cran-tree 1.0-29

by r-cran-robot - July 24, 2011, 00:00:00 CET [ Project Homepage BibTeX Download ] 7837 views, 1473 downloads, 1 subscription

About: Classification and regression trees


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 ] 7819 views, 1533 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.

  • 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 dataformat 0.1.1

by mikio - March 12, 2009, 16:07:55 CET [ Project Homepage BibTeX Download ] 7716 views, 1479 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.


Forgot to include the Java sources.

Logo mcmkl 0.1

by ong - May 15, 2008, 15:30:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7711 views, 1545 downloads, 1 subscription

About: We provide some preliminary code for multiclass multiple kernel learning in Matlab using CPLEX as a base solver.


Initial Announcement on

Showing Items 151-160 of 590 on page 16 of 59: First Previous 11 12 13 14 15 16 17 18 19 20 21 Next Last