Projects supporting the matlab data format.
Showing Items 21-40 of 72 on page 2 of 4: Previous 1 2 3 4 Next

Logo Accord.NET Framework 2.14.0

by cesarsouza - December 9, 2014, 23:04:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 37847 views, 6488 downloads, 2 subscriptions

About: The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details.


Adding a large number of new distributions, such as Anderson-Daring, Shapiro-Wilk, Inverse Chi-Square, Lévy, Folded Normal, Shifted Log-Logistic, Kumaraswamy, Trapezoidal, U-quadratic and BetaPrime distributions, Birnbaum-Saunders, Generalized Normal, Gumbel, Power Lognormal, Power Normal, Triangular, Tukey Lambda, Logistic, Hyperbolic Secant, Degenerate and General Continuous distributions.

Other additions include new statistical hypothesis tests such as Anderson-Daring and Shapiro-Wilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others.

For a complete list of changes, please see the full release notes at the release details page at:

Logo CFSPCommunityDetection 1.0

by tbuehler - October 13, 2014, 05:36:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3185 views, 797 downloads, 1 subscription

About: A community detection method based on constrained fractional set programming (CFSP).


Initial Announcement on

About: Toeblitz is a MATLAB/Octave package for operations on positive definite Toeplitz matrices. It can solve Toeplitz systems Tx = b in O(n*log(n)) time and O(n) memory, compute matrix inverses T^(-1) (with free log determinant) in O(n^2) time and memory, compute log determinants (without inverses) in O(n^2) time and O(n) memory, and compute traces of products A*T for any matrix A, in minimal O(n^2) time and memory.


Adding a write-up in written/toeblitz.pdf describing the package.

Logo pSpectralClustering 1.1

by tbuehler - July 30, 2014, 19:44:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9941 views, 2097 downloads, 2 subscriptions

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

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

Logo Crino 1.0.0

by jlerouge - July 16, 2014, 17:54:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3308 views, 799 downloads, 2 subscriptions

About: Crino: a neural-network library based on Theano


1.0.0 (7 july 2014) : - Initial release of crino - Implements a torch-like library to build artificial neural networks (ANN) - Provides standard implementations for : * auto-encoders * multi-layer perceptrons (MLP) * deep neural networks (DNN) * input output deep architecture (IODA) - Provides a batch-gradient backpropagation algorithm, with adaptative learning rate

Logo RankSVM NC 1.0

by rflamary - July 10, 2014, 15:51:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4646 views, 1150 downloads, 1 subscription

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


Initial Announcement on

Logo GradMC 2.00

by tur - April 14, 2014, 15:48:48 CET [ BibTeX Download ] 4989 views, 1542 downloads, 1 subscription

About: GradMC is an algorithm for MR motion artifact removal implemented in Matlab


Added support for multi-rigid motion correction.

Logo minFunc 2012

by markSchmidt - December 18, 2013, 01:07:07 CET [ Project Homepage BibTeX Download ] 5548 views, 1017 downloads, 1 subscription

About: minFunc is a Matlab function for unconstrained optimization of differentiable real-valued multivariate functions using line-search methods. It uses an interface very similar to the Matlab Optimization Toolbox function fminunc, and can be called as a replacement for this function. On many problems, minFunc requires fewer function evaluations to converge than fminunc (or minimize.m). Further it can optimize problems with a much larger number of variables (fminunc is restricted to several thousand variables), and uses a line search that is robust to several common function pathologies.


Initial Announcement on

About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.


added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes

generalised non-Gaussian potentials so that affine instead of linear functions of the latent variables can be used

Logo Multilinear Principal Component Analysis 1.3

by hplu - September 8, 2013, 13:04:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8504 views, 1592 downloads, 1 subscription

About: A Matlab implementation of Multilinear PCA (MPCA) and MPCA+LDA for dimensionality reduction of tensor data with sample code on gait recognition

  1. The MPCA paper is updated with a typo (the MAD measure in Table II) corrected.

  2. Tensor toolbox version 2.1 is included for convenience.

  3. Full code on gait recognition is included for verification and comparison.

About: Stochastic neighbor embedding originally aims at the reconstruction of given distance relations in a low-dimensional Euclidean space. This can be regarded as general approach to multi-dimensional scaling, but the reconstruction is based on the definition of input (and output) neighborhood probability alone. The present implementation also allows for handling dissimilarity or score-induced neighborhood topologies and makes use of quasi 2nd order gradient-based (l-)BFGS optimization.

  • gradient in xsne_fun.m fixed! (constant factor m was missing)

  • symmetry option re-introduced allowing for enabling symmetric and asymmetric versions of SNE and t-SNE

Logo cbMDS Correlation Based Multi Dimensional Scaling 1.2

by emstrick - July 27, 2013, 14:35:36 CET [ BibTeX BibTeX for corresponding Paper Download ] 8096 views, 1847 downloads, 1 subscription

About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships.

  • Initial release (Ver 1.0): Weighted Pearson and correlation and soft Spearman rank correlation, Tue Dec 4 16:14:51 CET 2012

  • Ver 1.1 Added soft Kendall correlation, Fri Mar 8 08:41:09 CET 2013

  • Ver 1.2 Added reconstruction of sparse relationship matrices, Fri Jul 26 16:58:37 CEST 2013

Logo Thalasso v0.2

by rherault - July 22, 2013, 15:33:59 CET [ Project Homepage BibTeX Download ] 2486 views, 710 downloads, 1 subscription

About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2||X*beta-y||^2 + lambda*sum|beta_i|, where X and y are problem data and beta and lambda are variables.


Initial Announcement on

About: This toolbox implements a novel visualization technique called Sectors on Sectors (SonS), and a extended version called Multidimensional Sectors on Sectors (MDSonS), for improving the interpretation of several data mining algorithms. The MDSonS method makes use of Multidimensional Scaling (MDS) to solve the main drawback of the previous method, namely, the lack of representing distances between pairs of clusters. These methods have been applied for visualizing the results of hierarchical clustering, Growing Hierarchical Self-Organizing Maps (GHSOM), classification trees and several manifolds. These methods make possible to extract all the existing relationships among centroids’ attributes at any hierarchy level.


Initial Announcement on

Logo OptWok 0.3.1

by ong - May 2, 2013, 10:46:11 CET [ Project Homepage BibTeX Download ] 12996 views, 2555 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.

  • minor bugfix

Logo ChaLearn Gesture Challenge Turtle Tamers 1.0

by konkey - March 17, 2013, 18:39:22 CET [ BibTeX BibTeX for corresponding Paper Download ] 2748 views, 961 downloads, 1 subscription

About: Soltion developed by team Turtle Tamers in the ChaLearn Gesture Challenge (


Initial Announcement on

Logo VLFeat 0.9.16

by andreavedaldi - October 5, 2012, 18:44:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12283 views, 2102 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 SVMStructMATLAB 1.2

by andreavedaldi - September 12, 2012, 00:25:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12906 views, 2308 downloads, 1 subscription

About: svm-struct-matlab is a MATLAB wrapper of T. Joachims' SVM^struct solver for structured output support vector machines.


Adds support for Xcode 4.0 and Mac OS X 10.7 and greater.

Logo SVM with uncertain labels 0.2

by rflamary - July 17, 2012, 11:06:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9384 views, 1912 downloads, 2 subscriptions

About: Matlab code for learning probabilistic SVM in the presence of uncertain labels.


Added missing dataset function (thanks to Hao Wu)

Logo Uncorrelated Multilinear Discriminant Analysis 1.0

by hplu - July 7, 2012, 06:27:56 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5427 views, 1127 downloads, 1 subscription

About: A Matlab implementation of Uncorrelated Multilinear Discriminant Analysis (UMLDA) for dimensionality reduction of tensor data via tensor-to-vector projection


Initial Announcement on

Showing Items 21-40 of 72 on page 2 of 4: Previous 1 2 3 4 Next