Heuristic Techniques for Constructing Hidden Markov Models of Stochastic Processes
Author:
Affiliation:
1. South-Russian State Polytechnic University (NPI) named after MI Platov,Dept. of Software Engineering,Novocherkassk,Russia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10110715/10110716/10110792.pdf?arnumber=10110792
Reference17 articles.
1. Early classifications of bearing faults using hidden Markov models, Gaussian mixture models, Mel-frequency cepstral coefficients and fractals;nelwamondo;International Journal of Innovative Computing Information and Control,2006
2. Fundamental Limits for Learning Hidden Markov Model Parameters
3. PyHHMM: A Python library for heterogeneous hidden Markov models;moreno-pino;Cornell University Open Access Archive,0
4. Deeptime: a Python library for machine learning dynamical models from time series data
5. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
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