Projects supporting the matlab data format.
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Logo JMLR GPstuff 4.4

by avehtari - April 15, 2014, 15:26:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7992 views, 2222 downloads, 1 subscription

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About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods.


2014-04-11 Version 4.4

New features

  • Monotonicity constraint for the latent function.

    • Riihimäki and Vehtari (2010). Gaussian processes with monotonicity information. Journal of Machine Learning Research: Workshop and Conference Proceedings, 9:645-652.
  • State space implementation for GP inference (1D) using Kalman filtering.

    • For the following covariance functions: Squared-Exponential, Matérn-3/2 & 5/2, Exponential, Periodic, Constant
    • Särkkä, S., Solin, A., Hartikainen, J. (2013). Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing. IEEE Signal Processing Magazine, 30(4):51-61.
    • Simo Sarkka (2013). Bayesian filtering and smoothing. Cambridge University Press.
    • Solin, A. and Särkkä, S. (2014). Explicit link between periodic covariance functions and state space models. AISTATS 2014.


  • GP_PLOT function for quick plotting of GP predictions
  • GP_IA now warns if it detects multimodal posterior distributions
  • much faster EP with log-Gaussian likelihood (numerical integrals -> analytical results)
  • faster WAIC with GP_IA array (numerical integrals -> analytical results)
  • New demos demonstrating new features etc.
    • demo_minimal, minimal demo for regression and classification
    • demo_kalman1, demo_kalman2
    • demo_monotonic, demo_monotonic2

Plus bug fixes

Logo GradMC 2.00

by tur - April 14, 2014, 15:48:48 CET [ BibTeX Download ] 983 views, 358 downloads, 1 subscription

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


Added support for multi-rigid motion correction.

Logo JMLR Information Theoretical Estimators 0.57

by szzoli - April 10, 2014, 18:35:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 32437 views, 6992 downloads, 2 subscriptions

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

  • Kullback-Leibler divergence estimation based on maximum likelihood estimation + analytical formula in the chosen exponential family: added.

  • A new sampling based entropy estimator with KDE correction on the left/right sides: added.

  • Quick tests: updated with the new estimators.

Logo Social Impact theory based Optimizer library 1.0.2

by rishem - March 24, 2014, 08:29:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1939 views, 474 downloads, 1 subscription

About: This is an optimization library based on Social Impact Theory(SITO). The optimizer works in the same way as PSO and GA.


A new variant 'Continuous Opinion Dynamics Optimizer (CODO)' has been implemented in this version. Minor changes in implementation of objective function.

Logo A Pattern Recognizer In Lua with ANNs v0.3.1-alpha

by pakozm - January 9, 2014, 22:09:03 CET [ Project Homepage BibTeX Download ] 1089 views, 272 downloads, 1 subscription

About: April-ANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising auto-encoders, convolutional neural networks), with other pattern recognition methods as hiddem makov models (HMMs) among others.


Added automatic differentiation package. Removed some bugs and memory leaks. Better decouplong between ANN modules, optimizer objects and loss functions. Addition of Conjugate Gradient, Rprop and Quickprop algorithms.

Logo JMLR Sally 0.8.2

by konrad - December 25, 2013, 13:38:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16281 views, 3264 downloads, 2 subscriptions

About: A Tool for Embedding Strings in Vector Spaces


Support for new version of libarchive. Several major and minor bug fixes.

Logo minFunc 2012

by markSchmidt - December 18, 2013, 01:07:07 CET [ Project Homepage BibTeX Download ] 568 views, 92 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

Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.4

by hn - November 11, 2013, 14:46:52 CET [ Project Homepage BibTeX Download ] 14727 views, 3888 downloads, 3 subscriptions

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About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

  • derivatives w.r.t. inducing points xu in infFITC, infFITC_Laplace, infFITC_EP so that one can treat the inducing points either as fixed given quantities or as additional hyperparameters
  • new GLM likelihood likExp for inter-arrival time modeling
  • new GLM likelihood likWeibull for extremal value regression
  • new GLM likelihood likGumbel for extremal value regression
  • new mean function meanPoly depending polynomially on the data
  • infExact can deal safely with the zero noise variance limit
  • support of GP warping through the new likelihood function likGaussWarp

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 Chalearn gesture challenge code by jun wan 1.0

by joewan - September 11, 2013, 07:32:51 CET [ BibTeX BibTeX for corresponding Paper Download ] 1486 views, 311 downloads, 0 subscriptions

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT, EMoSIFT and SMoSIFT features.


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Logo Multilinear Principal Component Analysis 1.3

by hplu - September 8, 2013, 13:04:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3006 views, 570 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.

Logo JMLR MSVMpack 1.4

by lauerfab - August 30, 2013, 10:40:35 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8930 views, 3235 downloads, 1 subscription

About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.

  • Added parallelized k-Fold cross validation ('-cv k' option in trainmsvm)
  • Cache size now defaults to the maximum amount of memory
  • Minor bug fixes for the Matlab interface

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 tar directly instead of via link

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 ] 2398 views, 679 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 ] 636 views, 172 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 Cognitive Foundry 3.3.3

by Baz - May 21, 2013, 05:59:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 14557 views, 2296 downloads, 2 subscriptions

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

  • General:
    • Made code able to compile under both Java 1.6 and 1.7. This required removing some potentially unsafe methods that used varargs with generics.
    • Upgraded XStream dependency to 1.4.4.
    • Improved support for regression algorithms in learning.
    • Added general-purpose adapters to make it easier to compose learning algorithms and adapt their input or output.
  • Common Core:
    • Added isSparse, toArray, dotDivide, and dotDivideEquals methods for Vector and Matrix.
    • Added scaledPlus, scaledPlusEquals, scaledMinus, and scaledMinusEquals to Ring (and thus Vector and Matrix) for potentially faster such operations.
    • Fixed issue where matrix and dense vector equals was not checking for equal dimensionality.
    • Added transform, transformEquals, tranformNonZeros, and transformNonZerosEquals to Vector.
    • Made LogNumber into a signed version of a log number and moved the prior unsigned implementation into UnsignedLogNumber.
    • Added EuclideanRing interface that provides methods for times, timesEquals, divide, and divideEquals. Also added Field interface that provides methods for inverse and inverseEquals. These interfaces are now implemented by the appropriate number classes such as ComplexNumber, MutableInteger, MutableLong, MutableDouble, LogNumber, and UnsignedLogNumber.
    • Added interface for Indexer and DefaultIndexer implementation for creating a zero-based indexing of values.
    • Added interfaces for MatrixFactoryContainer and DivergenceFunctionContainer.
    • Added ReversibleEvaluator, which various identity functions implement as well as a new utility class ForwardReverseEvaluatorPair to create a reversible evaluator from a pair of other evaluators.
    • Added method to create an ArrayList from a pair of values in CollectionUtil.
    • ArgumentChecker now properly throws assertion errors for NaN values. Also added checks for long types.
    • Fixed handling of Infinity in subtraction for LogMath.
    • Fixed issue with angle method that would cause a NaN if cosine had a rounding error.
    • Added new createMatrix methods to MatrixFactory that initializes the Matrix with the given value.
    • Added copy, reverse, and isEmpty methods for several array types to ArrayUtil.
    • Added utility methods for creating a HashMap, LinkedHashMap, HashSet, or LinkedHashSet with an expected size to CollectionUtil.
    • Added getFirst and getLast methods for List types to CollectionUtil.
    • Removed some calls to System.out and Exception.printStackTrace.
  • Common Data:
    • Added create method for IdentityDataConverter.
    • ReversibleDataConverter now is an extension of ReversibleEvaluator.
  • Learning Core:
    • Added general learner transformation capability to make it easier to adapt and compose algorithms. InputOutputTransformedBatchLearner provides this capability for supervised learning algorithms by composing together a triplet. CompositeBatchLearnerPair does it for a pair of algorithms.
    • Added a constant and identity learners.
    • Added Chebyshev, Identity, and Minkowski distance metrics.
    • Added methods to DatasetUtil to get the output values for a dataset and to compute the sum of weights.
    • Made generics more permissive for supervised cost functions.
    • Added ClusterDistanceEvaluator for taking a clustering that encodes the distance from an input value to all clusters and returns the result as a vector.
    • Fixed potential round-off issue in decision tree splitter.
    • Added random subspace technique, implemented in RandomSubspace.
    • Separated functionality from LinearFunction into IdentityScalarFunction. LinearFunction by default is the same, but has parameters that can change the slope and offset of the function.
    • Default squashing function for GeneralizedLinearModel and DifferentiableGeneralizedLinearModel is now a linear function instead of an atan function.
    • Added a weighted estimator for the Poisson distribution.
    • Added Regressor interface for evaluators that are the output of (single-output) regression learning algorithms. Existing such evaluators have been updated to implement this interface.
    • Added support for regression ensembles including additive and averaging ensembles with and without weights. Added a learner for regression bagging in BaggingRegressionLearner.
    • Added a simple univariate regression class in UnivariateLinearRegression.
    • MultivariateDecorrelator now is a VectorInputEvaluator and VectorOutputEvaluator.
    • Added bias term to PrimalEstimatedSubGradient.
  • Text Core:
    • Fixed issue with the start position for tokens from LetterNumberTokenizer being off by one except for the first one.

Logo OptWok 0.3.1

by ong - May 2, 2013, 10:46:11 CET [ Project Homepage BibTeX Download ] 6033 views, 1157 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 Download ] 672 views, 305 downloads, 1 subscription

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


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