DATA FUSION

Software

• BAYESLAB. This is a matlab toolbox written by Bin Shi, H.M. Chen, Ying Hung. The toolbox deals with Bayesian inference.
• BFD. This is a matlab toolbox written by T. Pena-Centemo, N. D. Lawrence. The toolbox deals with non-linear discriminant analysis.
• BNT. This is a matlab toolbox written by Kevin Patrick Murphy. The toolbox deals with building and analyzing Bayesian networks.
• DACE. This is a matlab toolbox written by Soren N. Lophaven, Hans Brun Nielsen and Jacob Sondergaard. The toolbox deals with Kriging Analysis.
• DTW. This is a matlab m-file written by Timothy Felty. The m-file implements a dynamic time warping algorithm.
• EFICA. This is a matlab m-file written by Zbynek Koldovsky and Petr Tichavsky. The m-file implements an independent component analysis algorithm.
• ENTOOL. This is a matlab toolbox written by C. Merkwirth and J. Wichard. The toolbox deals with ensemble modeling.
• FASTICA. This is a matlab toolbox written by Hugo Gavert, Jarmo Hurri, Jaakko Sarela and Aapo Hyvarinen. The toolbox deals with independent component analysis.
• GML. This is a matlab toolbox written by A. Vezhnevets and V. Vezhnevets. The toolbox deals with different boosting algorithms.
• GMMBAYES. This is a matlab toolbox written by Joni Kamarainen and Paalanen. The toolbox deals with the implementation of the Gaussian mixture model.
• GPCLASS. This is a matlab toolbox written by David Barber and C. K. I. Williams. The toolbox contains various algorithms for Bayesian classification.
• GPML. This is a matlab toolbox written by Carl Edward Rasmussen and Chris Williams. The toolbox deals with Gaussian regression and classification.
• GLMLAB. This is a matlab toolbox written by Peter K. Dunn. The toolbox deals with building and analyzing generalized linear models.
• HME. This is a matlab toolbox written by David R. Martin. The toolbox deals with the hierarchical mixture of experts model.
• LIBRA. This is a matlab toolbox written by Sabine Verboven and Mia Hubert. The toolbox contains an extensive collection of robust statistical techniques.
• LIBSVM. This is a matlab toolbox written by Chih-Chung Chang and Chih-Jen Lin. The toolbox deals with algorithms required for building and testing a support vector machine.
• KALMTOOL. This is a matlab toolbox written by Magnus Norgaard. The toolbox contains an implementation of the Kalman filter.
• KDE. This is a matlab toolbox written by Alex Ihler and Mike Mandel. The toolbox contains a suite of algorithms required for kernel density estimation.
• KFT. This is a matlab toolbox written by Kevin Patrick Murphy. The toolbox implements a Kalman filter.
• LIGHTSPEED. This is a matlab toolbox written by Tom Minka. The toolbox conatins a suite of very fast m-files which may be used to replace slow low-level matlab functions.
• MCMCSTUFF. This is a matlab toolbox written by Aki Vehtari. The toolbox contains the algorithms required to perform Bayesian inference using the MCMC technique.
• MILCA. This is a matlab toolbox written by Sergey Astakhov and Peter Grassberger, Alexander Kraskov and Harald Stogbauer. The toolbox contains a suite of algorithms for performing independent component analysis. The toolbox includes an accurate mutual information estimator.
• MIXLAB. This is a matlab toolbox written by Perry Moerland. This toolbox deals with the mixture of expert models.
• MUTIN. This is a matlab m-file written by Petr Tichavsky. The m-file implements an accurate mutual information estimator.
• NETLAB. This is a matlab toolbox written by Ian Nabney. This toolbox contains an extensive suite of m-fles for neural network pattern recognition.
• RADICAL. This is a matlab toolbox written by Erik G. Learned-Miller. This toolbox deals with independent component analysis.
• STPRTOOL. This is a matlab toolbox written by Vojtech Franc and Vaclav Hlavac. This toolbox contains an extensive suite of m-files for statistical pattern recognition.
• UPF. This is a matlab toolbox written by Nando de Freitas. This toolbox deals with the unscented particle filter.
• VBGP. This is a matlab toolbox written by Mark Girolami and Simon Rogers. This toolbox deals with variational Bayesian inference.