Have a personal or library account? Click to login
Synthesizing of models for identification of teletraffic Markov chains by artificial neural networks and decision tree method Cover

Synthesizing of models for identification of teletraffic Markov chains by artificial neural networks and decision tree method

Open Access
|Dec 2018

References

  1. [1] P. Sokólski and T. Rutkowski, “Hybrid of Neural Networks and Hidden Markov Models as a modern approach to speech recognition systems”, Pomiary Automatyka Robotyka, vol.17, no.2, pp.449-455, 2013.
  2. [2] M. Mustafa, T. Allen and K. Appiah, “A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition”, Springer, Neural Comput & Applic, pp.1-9, 2017.10.1007/s00521-017-3028-2
  3. [3] M. Yang, W. Tu, W. Yin and Z. Lu, “Deep Markov Neural Network for Sequential Data Classification”, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp.32-37, 2015.10.3115/v1/P15-2006
  4. [4] J. Bernstein, I. Dasgupta, D. Rolnick and H. Sompolinsky, “Markov Transitions between Attractor States in a Recurrent Neural Network”, AAAI Spring Symposium Series, Science of Intelligence: Computational Principles of Natural and Artificial Intelligence, pp.1-5, 2017.
  5. [5] D. Lowd and J. Davis, “Learning Markov Network Structure with Decision Trees”, IEEE International Conference on Data Mining, Sydney, NSW, pp.334-343, 2010.10.1109/ICDM.2010.128
  6. [6] D. Lowd and J. Davis, “Improving Markov Network Structure Learning Using Decision Trees”, Journal of Machine Learning Research (JMLR), no.15, pp.501-532, 2014.
  7. [7] B. Lakshminarayanan, D. Roy and Y. The, “Top-Down Particle Filtering for Bayesian Decision Trees”, Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA, JMLR: W&CP, vol.28, pp.1-9, 2013.
  8. [8] D. Bacciu, “Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data”, Proceedings of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Honolulu, HI, pp.1-8, 2017.10.1109/SSCI.2017.8285244
  9. [9] E. Ivanova, T. Iliev, Gr. Mihaylov and R. Rashkov, “Significant Simulation Parameters for RESTART/LRE Method in Teletraffic Systems of Network of Queues”, IN: Automation Control Theory Perspectives in Intelligent Systems, Springer, pp.325-336, 2016.10.1007/978-3-319-33389-2_31
  10. [10] E. Ivanova, T. Iliev, Gr. Mihaylov, I. Stoyanov, F. Tsvetanov, E. Otsetova and D. Radev, “Computer Modeling and Simulation of Teletraffic Models for 5G Networks”, 38th International Convention on Information and Telecommunication Technology, Electronics and Microelectronics-MIPRO, Croatia, pp.479-482, 2015.10.1109/MIPRO.2015.7160310
DOI: https://doi.org/10.2478/jee-2018-0055 | Journal eISSN: 1339-309X | Journal ISSN: 1335-3632
Language: English
Page range: 379 - 384
Submitted on: Jul 10, 2018
|
Published on: Dec 14, 2018
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year

© 2018 Ivelina Stefanova Balabanova, Georgi Ivanov Georgiev, Stanimir Michaylov Sadinov, Stela Savova Kostadinova, published by Slovak University of Technology in Bratislava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.