A Survey of Machine Learning Algorithms

Authors

  • Prof. Deepak Agrawal  Takshshila Institute of Engineering and Technology, Jabalpur, Madhya Pradesh, India
  • Abhiruchi Dubey  Takshshila Institute of Engineering and Technology, Jabalpur, Madhya Pradesh, India

Keywords:

ANN, Data Analytics, Machine Learning Algorithms, Technique, Prediction, Model

Abstract

Machine Learning is a booming research area in computer science and many other industries all over the world. It has gained great success in vast and varied application sectors. This includes social media, economy, finance, healthcare, agriculture, etc. Several intelligent machine learning techniques were designed and used to provide big data predictive analytics solutions. A literature survey of different machine learning techniques is provided in this paper. Also a study on commonly used machine learning algorithms for big data analytics is done and presented in this paper

References

  1. “Parallel machine learning toolbox”, retrieved from http://www.research.ibm.com/haifa/projects/verification/ml_tool box/.
  2. C. A. Caligtan and P. C. Dykes, “Electronic health records and personal health records”, Semin Oncol Nurs, vol. 27, pp. 218- 228, 2018.
  3. C.M. Bishop, Pattern recognition and machine learning, Springer, New York, 2019.
  4. D Peteiro-Barral and B Guijarro-Berdinas, “A survey of methods for distributed machine learning”, Progress in Artificial Intelligence, Springer, vol. 2, issue 1, pp. 1-11, 2018. DOI:10.1007/s13748-012-0035-5.
  5. G. Ding, Q. Wu, Y. D. Yao, J. Wang and Y. Chen, “Kernel- Based Learning for Statistical Signal Processing in Cognitive Radio Networks: Theoretical Foundations, Example Applications, and Future Directions”, IEEE Signal Processing Magazine, vol. 30, issue. 4, pp. 126-136, 2018. DOI: 10.1109/MSP.2013.2251071.
  6. J. L. Berral-Garcia, “A quick view on current techniques and machine learning algorithms for big data analytics”, 18th International Conf. on Transparent Optical Networks, pp.1-4, 2016. DOI: 10.1109/ICTON.2016.7550517.
  7. J. Qui, Q. Wu, G. Ding, Y. Xu and S. Feng, “A survey of machine learning for big data processing”, EURASIP Journal on Advances in Signal Processing, Springer, vol. 2016:67, pp. 1-16, 2016. DOI: 10.1186/s13634-016-0355-x.
  8. K Weiss, T Khoshgoftaar and D Wang, “A survey of transfer learning”, Journal of Big Data, Springer, vol. 3, issue 9, pp. 1- 40, 2016. DOI: 10.1186/s40537-016-0043-6
  9. Li Deng, “A tutorial survey of architectures, algorithms and applications for deep learning”, APSIPA transactions on Signal and Information Processing, vol. 3,pp.1-29,2014. DOI: https://doi.org/10.1017/atsip.2017.9.
  10. M. R. Bendre, R. C. Thool and V. R. Thool, “Big data in precision agriculture: Weather forecasting for future farming”, 1st International Conf. on Next Generation Computing Technologies, pp. 744-750, 2017.
  11. DOI:10.1109/NGCT.2015.7375220.
  12. M. U. Bokhari, M. Zeyauddin and M. A. Siddiqui, “An effective model for big data analytics”, 3rd International Conference on Computing for Sustainable Global Development, pp. 3980-3982, 2016.
  13. P. Y. Wu, C. W. Cheng, C. D. Kaddi, J. Venugopalan, R. Hoffman and M. D. Wang, “–Omic and Electronic Health Record Big Data Analytics for Precision Medicine”, IEEE Transactions on Biomedical Engineering, vol. 64, issue 2, pp. 263-273, 2017. DOI: 10.1109/TBME.2016.2573285.
  14. T. C. G. Atlas. Available: http://cancergenome.nih.gov/.
  15. W. Tu and S. Sun, “Cross-domain representation-learning framework with combination of class-separate and domain- merge objectives”, Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining , ACM, pp. 18–25, 2012. DOI: 10.1145/2351333.2351336.
  16. X. W. Chen and X. Lin, “Big Data Deep Learning: Challenges and Perspectives”, in IEEE Access, vol. 2, pp. 514-525, 2014. DOI: 10.1109/ACCESS.2014.2325029.
  17. Y. Bengio, A. Courville and P. Vincent, “Representation Learning: A Review and New Perspectives”, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, issue 8, pp. 1798-1828, 2017. DOI: 10.1109/TPAMI.2013.50.
  18. Y. Fu, B. Li, X. Zhu and C. Zhang, “Active Learning without Knowing Individual Instance Labels: A Pairwise Label Homogeneity Query Approach”, in IEEE Transactions on Knowledge and Data Engineering, vol. 26, issue 4, pp. 808-822, 2017. DOI: 10.1109/TKDE.2013.165.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-06-30

Issue

Volume 6, Issue 3, May-June-2019

Section

Research Articles

License

Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. Deepak Agrawal, Abhiruchi Dubey, " A Survey of Machine Learning Algorithms, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 3, pp.364-369, May-June-2019.