20 projects found that use octave as the programming language.
Showing Items 1-20 of 26 on page 1 of 2: 1 2 Next

Logo BayesOpt, a Bayesian Optimization toolbox 0.4.1

by rmcantin - May 15, 2013, 19:36:40 CET [ Project Homepage BibTeX Download ] 812 views, 196 downloads, 1 subscription

About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python.

Changes:

-Fixed bugs.

-Improved and extended documentation.

-Extended and simplified API accross platforms.

-Extended functionality (new surrogate functions, new priors, new kernels, new criteria).

-Improved modularity of the optimization process to allow plotting and debugging of intermediate steps.

-Added more demos and examples.


Logo Information Theoretical Estimators 0.37

by szzoli - May 12, 2013, 15:35:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10387 views, 2114 downloads, 1 subscription

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures and cross quantities. 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.

Changes:
  • K divergence estimation: added,

  • L divergence estimation: added,

  • kNN squared distance computation: refined.


Logo GPstuff 4.1

by avehtari - April 25, 2013, 11:07:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1820 views, 261 downloads, 1 subscription

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.

Changes:

2013-04-24 Version 4.1

New features:

  • Multinomial probit classification with nested-EP. Jaakko Riihimäki, Pasi Jylänki and Aki Vehtari (2013). Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood. Journal of Machine Learning Research 14:75-109, 2013.
  • Marginal posterior corrections for latent values. Cseke & Heskes (2011). Approximate Marginals in Latent Gaussian Models. Journal of Machine Learning Research 12 (2011), 417-454
    • Laplace: cm2 and fact
    • EP: fact

Improvements

  • lgpdens ignores now NaNs instead of giving error
  • gp_cpred has a new option 'target' accpeting values 'f' or 'mu'
  • unified gp_waic and gp_dic
    • by default return mlpd
    • option 'form' accetps now values 'mean' 'all' 'sum' and 'dic'
  • improved survival demo demo_survival_aft (accalerated failure time)
    • renamed and improved from demo_survival_weibull
  • rearranged some files to more logical directories
  • bug fixes

New files

  • gp_predcm: marginal posterior corrections for latent values.
  • demo_improvedmarginals: demonstration of marginal posterior corrections
  • demo_improvedmarginals2: demonstration of marginal posterior corrections
  • lik_multinomprobit: multinomial probit likelihood
  • demo_multiclass_nested_ep: demonstration of nested EP with multinomprobit

Logo cbMDS Correlation Based Multi Dimensional Scaling 1.1

by emstrick - March 11, 2013, 11:47:39 CET [ BibTeX BibTeX for corresponding Paper Download ] 892 views, 227 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 (asymmetric) distances, dissimilarities, or (negative) scores. Input-output relations are modelled as row-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships.

Changes:
  • 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


Logo JMLR GPML Gaussian Processes for Machine Learning Toolbox 3.2

by hn - January 21, 2013, 15:34:50 CET [ Project Homepage BibTeX Download ] 9773 views, 2721 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.

Changes:

We now support inference on large datasets using the FITC approximation for non-Gaussian likelihoods for EP and Laplace's approximation. New likelihood functions: mixture likelihood, Poisson likelihood, label noise. We added two MCMC samplers.


Logo LIBOL 0.1.0

by stevenhoi - December 27, 2012, 18:09:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2142 views, 135 downloads, 1 subscription

About: LIBOL is an open-source library that consists of a family of state-of-the-art online learning algorithms for machine learning and data mining research.

Changes:

Initial Announcement on mloss.org.


About: Stochastic neighbor embedding aims at the reconstruction of given distance, dissimilarity, or score neighborhood 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. Probability of score exceedance is used for neighborhood probability estimation, which is connected to soft-rank optimization. The present implementation makes use of quasi 2nd order gradient-based (l-)BFGS optimization.

Changes:
  • scoretoprob.m replaced by d2p.m

  • protein score data set added

  • trank.m computes (mid/max -tied) ranks along columns of matrix

  • local P- neighborhood probability estimation added

  • experimental soft_rank_SNE added for minimizing KL between probabilities of exceedance in source and embedding space

  • symmetry option removed, because this was strange in previous version


Logo JMLR libDAI 0.3.1

by jorism - September 17, 2012, 14:17:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 25719 views, 4773 downloads, 2 subscriptions

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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.1 fixes various bugs. The issues on 64-bit Windows platforms have been fixed and libDAI now offers full 64-bit support on all supported platforms (Linux, Mac OSX, Windows).


About: The package provides a Lagrangian approach to the posterior regularization of given linear mappings. This is important in two cases, (a) when systems are under-determined and (b) when the external model for calculating the mapping is invariant to properties such as scaling. The software may be applied in cases when the external model does not provide its own regularization strategy. In addition, the package allows to rank attributes according to their distortion potential to a given linear mapping.

Changes:

Version 1.1 (May 23, 2012) memory and time optimizations distderivrel.m now supports assessing the relevance of attribute pairs

Version 1.0 (Nov 9, 2011) * Initial Announcement on mloss.org.


Logo TMBP 1.0

by zengjia - April 5, 2012, 06:42:26 CET [ BibTeX BibTeX for corresponding Paper Download ] 1968 views, 973 downloads, 2 subscriptions

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About: Message passing for topic modeling

Changes:
  1. improve "readme.pdf".
  2. correct some compilation errors.

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

Changes:

Initial Announcement on mloss.org.


Logo MIToolbox 1.03

by apocock - February 13, 2012, 19:45:07 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5841 views, 1139 downloads, 1 subscription

About: A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, and Renyi entropy/mutual information. Works with discrete distributions, and expects column vectors of features.

Changes:

Updated documentation & link to JMLR publication.


Logo NaN toolbox 2.5.2

by schloegl - February 10, 2012, 11:45:52 CET [ Project Homepage BibTeX Download ] 21510 views, 4098 downloads, 1 subscription

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes:

Changes in v.2.5.2 - faster version of quantile if multiple quantiles are requested - removes the dependency on ZLIB and thus - fixes "pkg install nan" for Octave on Windows - a number of minor improvements

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG


Logo JMLR LWPR 1.2.4

by sklanke - February 6, 2012, 19:55:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22121 views, 2672 downloads, 1 subscription

About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...]

Changes:

Version 1.2.4

  • Corrected typo in lwpr.c (wrong function name for multi-threaded helper function on Unix systems) Thanks to Jose Luis Rivero

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.

Changes:

contributed by George Papandreou:

  • preconditioning support in the inf/linsolve_lcg.m CG routine.

  • @matConv2 and @matFD2 support different boundary conditions in deblurring

  • various mat/@*/diagFAtAFt.m support circulant preconditioning

  • bugfixes in nonnegativity option in pls/plsLBFGS.m and pen/penVBNorm.m when used together with EP

  • inf/diag_inv_sample.m, a Monte Carlo estimator

gfortran support to pls/lbfgsb/Makefile (thanks to Ernst Kloppenburg)

slight modification to mat/@matFFTN/mvm.m to make it more consistent

simple gradient solver using Barzilai-Borwein step size pls/plsBB.m


About: Multi-class vector classification based on cost function-driven learning vector quantization , minimizing misclassification.

Changes:

Initial Announcement on mloss.org.


Logo BRML toolbox 070711

by DavidBarber - July 17, 2011, 19:30:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 54665 views, 2777 downloads, 1 subscription

About: Bayesian Reasoning and Machine Learning toolbox

Changes:

Fixed some small bugs and updated some demos.


Logo Correlative Matrix Mapping, CMM 1.1

by emstrick - July 5, 2011, 15:15:21 CET [ BibTeX BibTeX for corresponding Paper Download ] 3118 views, 696 downloads, 1 subscription

About: Correlative Matrix Mapping (CMM) provides a supervised linear data mapping into a Euclidean subspace of given dimension. Applications include denoising, visualization, label-specific data preprocessing, and assessment of data attribute pairs relevant for the supervised mapping. Solving auto-association problems yields linear multidimensional scaling, similar to PCA, but usually with more faithful low-dimensional mappings.

Changes:

Tue Jul 5 14:40:03 CEST 2011 - Bugfixes and cleanups

  • single precision data affected pinv(). Now fairer using double precision.
  • early stopping did not work properly; now fixed
  • Hessian update mode globally controlled via hessmode, 'lbfgs' / 'bfgs'
  • distmat.m corrected for rounding problems and extended to distmat(X,Y)
  • replaced files: corv.m + corvgrad.m -> corvg.m
  • removed unused files: corrmat.m, splitdata.m, traforankapply.m

Logo Hidden Markov Support Vector Machines 0.2

by pramod - April 16, 2010, 17:27:41 CET [ BibTeX Download ] 3724 views, 849 downloads, 1 subscription

About: This software is an implementation of Hidden Markov Support Vector Machines (HMSVMs).

Changes:

Initial Announcement on mloss.org.


Logo JMLR Matlab toolbox for submodular function optimization 2.0

by krausea - April 7, 2010, 09:53:40 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9624 views, 3263 downloads, 1 subscription

About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others.

Changes:
  • Modified specification of optional parameters (using sfo_opt)
  • Added sfo_ls_lazy for maximizing nonnegative submodular functions
  • Added sfo_fn_infogain, sfo_fn_lincomb, sfo_fn_invert, ...
  • Added additional documentation and more examples
  • Now Octave ready

Showing Items 1-20 of 26 on page 1 of 2: 1 2 Next