Projects supporting the libsvm data format.


Logo Somoclu 1.4.1

by peterwittek - January 28, 2015, 13:19:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5638 views, 1059 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 v0.3.9

by crowwork - January 21, 2015, 19:33:24 CET [ Project Homepage BibTeX Download ] 3825 views, 734 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. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

Changes:

New features in the lastest changes

  • Distributed version that scale xgboost to even larger problems with cluster

  • Feature importance visualization in R module

  • Predict leaf index


Logo python weka wrapper 0.2.2

by fracpete - January 5, 2015, 03:43:56 CET [ Project Homepage BibTeX Download ] 8492 views, 1802 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • added convenience methods "no_class" (to unset class) and "has_class" (class set?) to "Instances" class
  • switched to using faster method objects for methods "classify_instance"/"distribution_for_instance" in "Classifier" class
  • switched to using faster method objects for methods "cluster_instance"/"distribution_for_instance" in "Clusterer" class
  • switched to using faster method objects for methods "class_index", "is_missing", "get/set_value", "get/set_string_value", "weight" in "Instance" class
  • switched to using faster method objects for methods "input", "output", "outputformat" in "Filter" class
  • switched to using faster method objects for methods "attribute", "attribute_by_name", "num_attributes", "num_instances", "class_index", "class_attribute", "set_instance", "get_instance", "add_instance" in "Instances" class

Logo ADAMS 0.4.7

by fracpete - December 24, 2014, 02:57:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8837 views, 2022 downloads, 2 subscriptions

About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows.

Changes:
  • 51 new actors
  • 16 new conversions
  • new module adams-jooq: code generation from JDBC databases for typed access
  • new module adams-image-webservice: allows upload of images using webservice
  • adams-timeseries module extended
  • adams-spreadsheet module extended
  • adams-random module extended
  • adams-imaging module overhaul

Logo JMLR JKernelMachines 2.5

by dpicard - December 11, 2014, 17:51:42 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15036 views, 3620 downloads, 4 subscriptions

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About: machine learning library in java for easy development of new kernels

Changes:

Version 2.5

  • New active learning algorithms
  • Better threading management
  • New multiclass SVM algorithm based on SDCA
  • Handle class balancing in cross-validation
  • Optional support of EJML switch to version 0.26
  • Various bugfixes and improvements

Logo WolfeSVM 0.0

by utmath - November 19, 2014, 10:46:11 CET [ Project Homepage BibTeX Download ] 436 views, 100 downloads, 2 subscriptions

About: This is a library for solving nu-SVM by using Wolfe's minimum norm point algorithm. You can solve binary classification problem.

Changes:

Initial Announcement on mloss.org.


Logo LIBOL 0.3.0

by stevenhoi - December 12, 2013, 15:26:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8950 views, 2990 downloads, 2 subscriptions

About: LIBOL is an open-source library with a family of state-of-the-art online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification.

Changes:

In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows:

• Add a template and guide for adding new algorithms;

• Improve parameter settings and make documentation clear;

• Improve documentation on data formats and key functions;

• Amend the "OGD" function to use different loss types;

• Fixed some name inconsistency and other minor bugs.


Logo OpenANN 1.1.0

by afabisch - September 26, 2013, 23:52:03 CET [ Project Homepage BibTeX Download ] 2931 views, 621 downloads, 2 subscriptions

About: A library for artificial neural networks.

Changes:

Added algorithms:

  • L-BFGS optimizer
  • k-means
  • sparse auto-encoder
  • preprocessing: normalization, PCA, ZCA whitening

Logo Nen Beta

by pascal - February 19, 2012, 00:31:34 CET [ Project Homepage BibTeX Download ] 3380 views, 1020 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.