Acquaintances in Machine Learning - Considering the Integration of Tech-Facts

Authors(3) :-Pramod Talole, Dipak Mathe, Yogeshwari Rajure

Contempt the good successes of machine learning, it can have its limits when handling insufficient training data. A potential solution is to include additional knowledge into the training process which results in the thought of assured machine learning. The topic covers search survey and structured overview of varied solutions in this field. This aims to determine taxonomy which may function a classification framework that considers the type of extra knowledge, its representation, and its combination into the machine learning pipeline. The evaluation of various papers on the bases of the taxonomy uncovers key ways of this field.

Authors and Affiliations

Pramod Talole
Department of Information Technology, Anuradha Engineering College, Chikhli, Maharashtra, India
Dipak Mathe
Department of Information Technology, Anuradha Engineering College, Chikhli, Maharashtra, India
Yogeshwari Rajure
Department of Information Technology, Anuradha Engineering College, Chikhli, Maharashtra, India

Machine Learning, Evaluation, Classification, Taxonomy, Structured, Potential.

  1. A. Conneau, H. Schwenk, L. Barrault, and Y. Lecun, “Very deep convolutional networks for text classification,” arXiv preprint arXiv:1606.01781, 2016.
  2. D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al., “Mastering the game of go with deep neural networks and tree search,” nature, vol. 529, no. 7587, 2016.
  3. K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, “Machine learning for molecular and materials science,” Nature, vol. 559, no. 7715, p. 547, 2018.
  4. T. Ching, D. S. Himmelstein, B. K. Beaulieu-Jones, A. A. Kalinin, B. T. Do, G. P. Way, E. Ferrero, P.-M. Agapow, M. Zietz, M. M. Hoffman et al., “Opportunities and obstacles for deep learning in biology and medicine,” Journal of The Royal Society Interface, vol. 15, no. 141, p. 20170387, 2018.
  5. J. N. Kutz, “Deep learning in fluid dynamics,” Journal of Fluid Mechanics, vol. 814, pp. 1–4, 2017.
  6. G. G. Towell and J. W. Shavlik, “Knowledge-based artificial neural networks,” Artificial Intelligence, vol. 70, no. 1-2, 1994.
  7. Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. Xing, “Harnessing deep neural networks with logic rules,” arXiv preprint arXiv:1603.06318, 2016.
  8. M. Diligenti, M. Gori, and C. Sacca, “Semantic-based regularization for learning and inference,” Artificial Intelligence, vol. 244, 2017.
  9. M. Diligenti, S. Roychowdhury, and M. Gori, “Integrating prior knowledge into deep learning,” in 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017.
  10. A. Karpatne, W. Watkins, J. Read, and V. Kumar, “Physics-guided neural networks (pgnn): An application in lake temperature modeling,” arXiv preprint arXiv:1710.11431, 2017.
  11. R. Stewart and S. Ermon, “Label-free supervision of neural networks with physics and domain knowledge,” in Proceedings of the 31st AAAI Conference on Artificial Intelligence. AAAI Press, 2017.
  12. P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende et al., “Interaction networks for learning about objects, relations and physics,” in Advances in Neural Information Processing Systems (NIPS), 2016.
  13. K. Marino, R. Salakhutdinov, and A. Gupta, “The more you know: Using knowledge graphs for image classification,” arXiv preprint arXiv:1612.04844, 2016.
  14. A. J. Ratner, C. M. De Sa, S. Wu, D. Selsam, and C. Ré, “Data programming: Creating large training sets, quickly,” in Advances in Neural Information Processing Systems (NIPS), 2016.
  15. M. Sachan, K. A. Dubey, T. M. Mitchell, D. Roth, and E. P. Xing, “Learning pipelines with limited data and domain knowledge: A studyin parsing physics problems,” in Advances in Neural Information Processing Systems (NIPS), 2018.

Publication Details

Published in : Volume 5 | Issue 6 | January-February 2020
Date of Publication : 2020-02-17
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 273-278
Manuscript Number : IJSRST210250
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Pramod Talole, Dipak Mathe, Yogeshwari Rajure, " Acquaintances in Machine Learning - Considering the Integration of Tech-Facts", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 5, Issue 6, pp.273-278, January-February-2020.
Journal URL : https://ijsrst.com/IJSRST210250
Citation Detection and Elimination     |      | | BibTeX | RIS | CSV

Article Preview