Kernel Density Estimation Toolbox for MATLAB. The KDE class is a general matlab class for k-dimensional kernel density estimation. It is written in a mix of matlab ”.m” files and MEX/C++ code. Thus, to use it you will need to be able to compile C++ code for Matlab. Note that the default compiler for Windows does not support C++, so you will need GCC under Linux, or GCC or Visual C++ for Windows. Bloodshed supplies a nice development environment along with the MinGW compiler.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Loxley, P. N.: The two-dimensional Gabor function adapted to natural image statistics: a model of simple-cell receptive fields and sparse structure in images (2017)
- Chen, Fei; Yu, Huimin; Yao, Jincao; Hu, Roland: Robust sparse kernel density estimation by inducing randomness (2015)
- Jing, Junmei; Koch, Inge; Naito, Kanta: Polynomial histograms for multivariate density and mode estimation (2012)
- Denton, Anne M.; Besemann, Christopher A.; Dorr, Dietmar H.: Pattern-based time-series subsequence clustering using radial distribution functions (2009) ioport
- Marzouk, Youssef; Xiu, Dongbin: A stochastic collocation approach to Bayesian inference in inverse problems (2009)
- Horta, L. G.; Kenny, S. P.; Crespo, L. G.; Elliott, K. B.: NASA Langley’s approach to the Sandia’s structural dynamics challenge problem (2008)
- Mandel, Michael I.; Poliner, Graham E.; Ellis, Daniel P. W.: Support vector machine active learning for music retrieval (2006) ioport