1D matrix classification using hidden markov model based machine learning for 3 class problems. It also consist of a matrix-based example of input sample of size 15 and 3 features
Hidden Markov Model (HMM) Toolbox for Matlab
Written by Kevin Murphy, 1998.
Last updated: 8 June 2005.
Distributed under the MIT License
This toolbox supports inference and learning for HMMs with discrete outputs (dhmm's), Gaussian outputs (ghmm's), or mixtures of Gaussians output (mhmm's). The Gaussians can be full, diagonal, or spherical (isotropic). It also supports discrete inputs, as in a POMDP. The inference routines support filtering, smoothing, and fixed-lag smoothing.
Selva (2021). Tutorial for classification by Hidden markov model (https://www.mathworks.com/matlabcentral/fileexchange/72594-tutorial-for-classification-by-hidden-markov-model), MATLAB Central File Exchange. Retrieved .
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