SimML Framework: Monte Carlo Simulation of Statistical Machine Learning Algorithms for IoT Prognostic Applications

@article{More2016SimMLFM,
  title={SimML Framework: Monte Carlo Simulation of Statistical Machine Learning Algorithms for IoT Prognostic Applications},
  author={Ashwini R. More and Kenny C. Gross},
  journal={2016 International Conference on Computational Science and Computational Intelligence (CSCI)},
  year={2016},
  pages={174-179}
}
Advanced statistical machine learning (ML) algorithms are being developed, trained, tuned, optimized, and validated for real-time prognostics for internet-of-things (IoT) applications in the fields of manufacturing, transportation, and utilities. For such applications, we have achieved greatest prognostic success with ML algorithms from a class of pattern recognition known as nonlinear, nonparametric regression. To intercompare candidate ML algorithmics to identify the "best" algorithms for IoT… CONTINUE READING

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