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Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.0.0) |
| Published: | 2018-11-02 |
| Author: | Muhammad Kashif Hanif [cre, aut], Muhammad Umer Sarwar [aut], Rehman Ahmad [aut], Zeeshan Ahmad [aut], Karl-Heinz Zimmermann [aut] |
| Maintainer: | Muhammad Kashif Hanif <mkashifhanif at gcuf.edu.pk> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| CRAN checks: | SAutomata results |
| Reference manual: | SAutomata.pdf |
| Package source: | SAutomata_0.1.0.tar.gz |
| Windows binaries: | r-devel: SAutomata_0.1.0.zip, r-release: SAutomata_0.1.0.zip, r-oldrel: SAutomata_0.1.0.zip |
| macOS binaries: | r-release (arm64): SAutomata_0.1.0.tgz, r-oldrel (arm64): SAutomata_0.1.0.tgz, r-release (x86_64): SAutomata_0.1.0.tgz, r-oldrel (x86_64): SAutomata_0.1.0.tgz |
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