A Survey on Machine Learning: Concept, Algorithms, and Applications

Authors

  • Sakshini Hangloo  M.Tech, Department of computer science, Shri Mata Vaishno Devi University, J&K, India
  • Samreen Kour  M.Tech, Department of computer science, Shri Mata Vaishno Devi University, J&K, India
  • Sudesh Kumar  PhD Scholar, Department of computer science, Shri Mata Vaishno Devi University, J&K, India

Keywords:

Machine Learning, Precision, Training data, Procedures

Abstract

In today's era machine learning concepts and algorithms are heavily used in the digital world. Machine learning algorithms can easily understand how to perform important tasks by generalizing from examples. Machine learning is often feasible and cost-effective approach where manual programming is not. From the past few decagons, Machine learning (ML) made software application more accurate to predict outputs. Also, various algorithms that are designed in machine learning are continuously used for pattern recognition, data clarification, and various other plans and have lead to a distinct research in data mining to determine underground consistencies or inconsistencies in collective data. The main objective of this paper is to discuss various concepts, approaches and procedures of machine learning used in addressing the digital world problems.

References

  1. Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng., Convolution Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations, Proceedings of the 26th Annual International Conference on Machine Learning, 2009.
  2. Kajaree Das, Rabi Narayan Behera., a Survey on Machine Learning: Concept, Algorithms and Application, International Journal of Innovative Research in Computer, and Communication Engineering, Feb 2017.
  3. N. Cristianini and J. Shawe-Taylor, an Introduction to Support Vector Machines. Cambridge University Press, 2000.
  4. E. Osuna, R. Freund, and F. Girosi. Support vector machines: training and applications. AI Memo 1602, MIT, May 1997.
  5. Taiwo Oladipupo Ayodele, Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zhang (Ed.), InTech, 2010
  6. T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K.Mazaitis, T. Mohamed, N. Nakashole, E. Platanios,A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov,M.Greaves, J. Welling, Never-Ending Learning, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2014
  7. Wang, J. and Jebara, T. and Chang, S.-F. Semi-supervised learning using greedy max- cut.Journal of Machine Learning Research , Volume 14(1), 771-800 2013
  8. Chapelle, O. and Sindhwani, V. and Keerthi, S. S. Optimization Techniques for Semi- Supervised Support Vector Machines, Journal of Machine Learning Research , Volume 9, 203–233, 2013
  9. J. Baxter, A model of inductive bias learning. Journal of Artificial Intelligence Research, 12:149–198, 2000.
  10. S. Ben-David and R. Schuller, Exploiting task relatedness for multiple task learning, Conference on Learning Theory, 2003.
  11. W. Dai, G. Xue, Q. Yang, and Y. Yu, Transferring Naive Bayes classifiers for text classification. AAAI Conference on Artificial Intelligence, 2007.
  12. Z. Marx, M. Rosenstein, L. Kaelbling, and T. Dietterich. Transfer learning with an ensemble of background tasks. In NIPS Workshop on Transfer Learning, 2005.
  13. R Conway and D Strip, Selective partial access to a database, In Proceedings of ACM Annual Conference, 85 - 89, 1976
  14. P D Stachour and B M Thuraisingham Design of LDV A multilevel secure relational database management system, IEEE Trans. Knowledge and Data Eng., Volume 2, Issue 2, 190 - 209, 1990
  15. R Oppliger, Internet security: Firewalls and beyond, Comm. ACM, Volume 40, Issue 5, 92 -102, 1997
  16. Rakesh Agrawal, Ramakrishnan Srikant, Privacy Preserving Data Mining, SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data, Volume 29 Issue 2,Pages 439-450, 2000
  17. A. Carlson, J. Betteridge, B.Kisiel, B.Settles,E. R.Hruschka Jr,and T. M. Mitchell, Toward an architecture for never-ending language learning, AAAI, volume 5, 3, 2010
  18. X. Chen, A. Shrivastava, and A. Gupta, Neil: Extracting visual knowledge from web data, In Proceedings of ICCV, 2013.
  19. P. Donmezand J. G. Carbonell, Proactive learning: cost-sensitive active learning with multiple imperfect oracles. In proceedings of the 17th ACM conference on information and knowledge management, 619–628. ACM, 2008
  20. T. M.Mitchell, J. Allen, P. Chalasani, J. Cheng, O. Etzioni, M. N. Ringuetteand J. C. Schlimmer, Theo: A framework for self-improving systems, Arch. for Intelligence 323–356, 1991
  21. Gregory, P. A. and Gail, A. C. Self-supervised ARTMAP Neural Networks, Volume 23, 265-282, 2010
  22. Cour, T. and Sapp, B. and Taskar, B. Learning from partial labels, Journal of Machine Learning Research, Volume 12, 1501-1536 2012
  23. Adankon, M. and Cheriet, M. Genetic algorithm-based training for semi-supervised SVM, Neural Computing and Applications , Volume 19(8), 1197-1206, 2010
  24. T. M. Mitchell (1997), Machine Learning, McGraw-Hill International.
  25. Stuart Russell, Peter Norvig (04-Jul-2016 ), Artificial Intelligence, A Modern Approach, Global Edition, Pearson Education Limited.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2017-09-30

Issue

Volume 2, Issue 7, September-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Sakshini Hangloo, Samreen Kour, Sudesh Kumar, " A Survey on Machine Learning: Concept, Algorithms, and Applications, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.293-301, September-2017.