Projects that are tagged with mkl.


Logo JMLR JKernelMachines 3.0

by dpicard - May 4, 2016, 17:53:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 42550 views, 8990 downloads, 4 subscriptions

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

Changes:

Version 3.0

/! Warning: this version is incompatible with previous code

  • change license to BSD 3-clauses
  • change package name to net.jkernelmachines
  • change to maven build system (available through central)
  • online training interfaces to allow continuous online learning
  • add a new budget oriented kernel classifier
  • new kernel and processing especially for strings

Logo JMLR SHOGUN 4.0.0

by sonne - February 5, 2015, 09:09:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 134177 views, 19181 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 RankSVM NC 1.0

by rflamary - July 10, 2014, 15:51:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5141 views, 1246 downloads, 1 subscription

About: This package is an implementation of a linear RankSVM solver with non-convex regularization.

Changes:

Initial Announcement on mloss.org.


Logo MShadow 1.0

by antinucleon - April 10, 2014, 02:57:54 CET [ Project Homepage BibTeX Download ] 3681 views, 935 downloads, 1 subscription

About: Lightweight CPU/GPU Matrix/Tensor Template Library in C++/CUDA. Support element-wise expression expand in high performance. Code once, run smoothly on both GPU and CPU

Changes:

Initial Announcement on mloss.org.


Logo Sparse MultiTask Learning Toolbox 1.2

by rflamary - March 18, 2012, 11:31:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7073 views, 1487 downloads, 1 subscription

About: This package is a set of Matlab scripts that implements the algorithms described in the submitted paper: "Lp-Lq Sparse Linear and Sparse Multiple Kernel MultiTask Learning".

Changes:

Initial Announcement on mloss.org.


Logo RLS2 MATLAB Toolbox 0.7

by posaune - March 31, 2010, 20:37:11 CET [ Project Homepage BibTeX Download ] 12046 views, 2461 downloads, 1 subscription

About: RLS2 is an instance of multiple kernel learning algorithm to simultaneously learn a regularized predictor and the kernel function. RLS2LIN is a version of RLS2 specialized to linear kernels on each feature. The package contains a set of scripts that implements RLS2 and RLS2LIN, together with a Graphic User Interface to load data, perform training, validation, and plot results.

Changes:
  • New kernel functions (rbfall, rbfsingle, polyall, polysingle)
  • Improved interface for pre-processing operations
  • The interface now allows to disable bias
  • Fixed bugs in parameter passing (thanks to Andrea Schirru)

Logo MPI IKL 1.0

by pgehler - January 16, 2009, 16:39:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9785 views, 1868 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 mcmkl 0.1

by ong - May 15, 2008, 15:30:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10275 views, 2098 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.