Projects supporting the hdf data format.


Logo JMLR MLPACK 2.0.3

by rcurtin - July 22, 2016, 00:39:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 64721 views, 11671 downloads, 6 subscriptions

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

Changes:
  • Standardize some parameter names for programs (old names are kept for reverse compatibility, but warnings will now be issued).
  • RectangleTree optimizations (#721).
  • Fix memory leak in NeighborSearch (#731).
  • Documentation fix for k-means tutorial (#730).
  • Fix TreeTraits for BallTree (#727).
  • Fix incorrect parameter checks for some command-line programs.
  • Fix error in HMM training with probabilities for each point (#636).

Logo Armadillo library 7.200

by cu24gjf - July 10, 2016, 15:44:07 CET [ Project Homepage BibTeX Download ] 90878 views, 18174 downloads, 5 subscriptions

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About: Armadillo is a high quality C++ linear algebra library, aiming towards a good balance between speed and ease of use. The function syntax is deliberately similar to MATLAB. Useful for algorithm development directly in C++, or quick conversion of research code into production environments (eg. software & hardware products).

Changes:
  • eigs_sym(), eigs_gen() and svds() now use a built-in reimplementation of ARPACK; contributed by Yixuan Qiu
  • faster handling of compound expressions by vectorise()
  • added .index_min() and .index_max()
  • added erf(), erfc(), lgamma()
  • added .head_slices() and .tail_slices() to subcube views
  • expanded ind2sub() to handle vectors of indices
  • expanded sub2ind() to handle matrix of subscripts
  • expanded expmat(), logmat() and sqrtmat() to optionally return a bool indicating success
  • spsolve() now requires SuperLU 5.2

Logo BayesPy 0.4.1

by jluttine - November 2, 2015, 13:40:09 CET [ Project Homepage BibTeX Download ] 15379 views, 3456 downloads, 3 subscriptions

About: Variational Bayesian inference tools for Python

Changes:
  • Define extra dependencies needed to build the documentation

Logo JMLR SHOGUN 4.0.0

by sonne - February 5, 2015, 09:09:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 114982 views, 16328 downloads, 6 subscriptions

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About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line.

Changes:

This release features the work of our 8 GSoC 2014 students [student; mentors]:

  • OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]
  • Large-Scale Multi-Label Classification [Abinash Panda; Thoralf Klein]
  • Large-scale structured prediction with approximate inference [Jiaolong Xu; Shell Hu]
  • Essential Deep Learning Modules [Khaled Nasr; Sergey Lisitsyn, Theofanis Karaletsos]
  • Fundamental Machine Learning: decision trees, kernel density estimation [Parijat Mazumdar ; Fernando Iglesias]
  • Shogun Missionary & Shogun in Education [Saurabh Mahindre; Heiko Strathmann]
  • Testing and Measuring Variable Interactions With Kernels [Soumyajit De; Dino Sejdinovic, Heiko Strathmann]
  • Variational Learning for Gaussian Processes [Wu Lin; Heiko Strathmann, Emtiyaz Khan]

It also contains several cleanups and bugfixes:

Features

  • New Shogun project description [Heiko Strathmann]
  • ID3 algorithm for decision tree learning [Parijat Mazumdar]
  • New modes for PCA matrix factorizations: SVD & EVD, in-place or reallocating [Parijat Mazumdar]
  • Add Neural Networks with linear, logistic and softmax neurons [Khaled Nasr]
  • Add kernel multiclass strategy examples in multiclass notebook [Saurabh Mahindre]
  • Add decision trees notebook containing examples for ID3 algorithm [Parijat Mazumdar]
  • Add sudoku recognizer ipython notebook [Alejandro Hernandez]
  • Add in-place subsets on features, labels, and custom kernels [Heiko Strathmann]
  • Add Principal Component Analysis notebook [Abhijeet Kislay]
  • Add Multiple Kernel Learning notebook [Saurabh Mahindre]
  • Add Multi-Label classes to enable Multi-Label classification [Thoralf Klein]
  • Add rectified linear neurons, dropout and max-norm regularization to neural networks [Khaled Nasr]
  • Add C4.5 algorithm for multiclass classification using decision trees [Parijat Mazumdar]
  • Add support for arbitrary acyclic graph-structured neural networks [Khaled Nasr]
  • Add CART algorithm for classification and regression using decision trees [Parijat Mazumdar]
  • Add CHAID algorithm for multiclass classification and regression using decision trees [Parijat Mazumdar]
  • Add Convolutional Neural Networks [Khaled Nasr]
  • Add Random Forests algorithm for ensemble learning using CART [Parijat Mazumdar]
  • Add Restricted Botlzmann Machines [Khaled Nasr]
  • Add Stochastic Gradient Boosting algorithm for ensemble learning [Parijat Mazumdar]
  • Add Deep contractive and denoising autoencoders [Khaled Nasr]
  • Add Deep belief networks [Khaled Nasr]

Bugfixes

  • Fix reference counting bugs in CList when reference counting is on [Heiko Strathmann, Thoralf Klein, lambday]
  • Fix memory problem in PCA::apply_to_feature_matrix [Parijat Mazumdar]
  • Fix crash in LeastAngleRegression for the case D greater than N [Parijat Mazumdar]
  • Fix memory violations in bundle method solvers [Thoralf Klein]
  • Fix fail in library_mldatahdf5.cpp example when http://mldata.org is not working properly [Parijat Mazumdar]
  • Fix memory leaks in Vowpal Wabbit, LibSVMFile and KernelPCA [Thoralf Klein]
  • Fix memory and control flow issues discovered by Coverity [Thoralf Klein]
  • Fix R modular interface SWIG typemap (Requires SWIG >= 2.0.5) [Matt Huska]

Cleanup and API Changes

  • PCA now depends on Eigen3 instead of LAPACK [Parijat Mazumdar]
  • Removing redundant and fixing implicit imports [Thoralf Klein]
  • Hide many methods from SWIG, reducing compile memory by 500MiB [Heiko Strathmann, Fernando Iglesias, Thoralf Klein]

Logo OptWok 0.3.1

by ong - May 2, 2013, 10:46:11 CET [ Project Homepage BibTeX Download ] 11476 views, 2262 downloads, 1 subscription

About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in: - Ellipsoidal multiple instance learning - difference of convex functions algorithms for sparse classfication - Contextual bandits upper confidence bound algorithm (using GP) - learning output kernels, that is kernels between the labels of a classifier.

Changes:
  • minor bugfix

Logo OpenGM 2 2.0.2 beta

by opengm - June 1, 2012, 14:33:53 CET [ Project Homepage BibTeX Download ] 4068 views, 969 downloads, 1 subscription

About: A C++ Library for Discrete Graphical Models

Changes:

Initial Announcement on mloss.org.


Logo Marray 2.2

by andres - July 6, 2011, 01:27:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4611 views, 1098 downloads, 1 subscription

About: Fast Runtime-Flexible Multi-dimensional Arrays and Views for C++

Changes:

Initial Announcement on mloss.org.


Logo mldata.org svn-r1070-Apr-2011

by sonne - April 8, 2011, 10:15:49 CET [ Project Homepage BibTeX Download ] 5361 views, 1232 downloads, 1 subscription

About: The source code of the mldata.org site - a community portal for machine learning data sets.

Changes:

Initial Announcement on mloss.org.


Logo mldata-utils 0.5.0

by sonne - April 8, 2011, 10:02:44 CET [ Project Homepage BibTeX Download ] 30554 views, 6673 downloads, 1 subscription

About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org

Changes:
  • Change task file format, such that data splits can have a variable number items and put into up to 256 categories of training/validation/test/not used/...
  • Various bugfixes.

About: OpenGM is a free C++ template library, a command line tool and a set of MATLAB functions for optimization in higher order graphical models. Graphical models of any order and structure can be built either in C++ or in MATLAB, using simple and intuitive commands. These models can be stored in HDF5 files and subjected to state-of-the-art optimization algorithms via the OpenGM command line optimizer. All library functions can also be called directly from C++ code. OpenGM realizes the Inference Algorithm Interface (IAI), a concept that makes it easy for programmers to use their own algorithms and factor classes with OpenGM.

Changes:

Initial Announcement on mloss.org.