A Study on Machine Learning: Elements, Characteristics and Algorithms

  • Authors

    • K Chokkanathan
    • S Koteeswaran
    2018-04-17
    https://doi.org/10.14419/ijet.v7i2.19.13793
  • Deterministic, network analysis, real time and non-real time traffic, spam filters.
  • Abstract

    Machine learning algorithms are used immensely for performing most important computational tasks with the help of sample data sets.  Most of the cases Machine learning algorithms will provide best solution where the programming languages failed to produce viable and economically cost-effective results.  Huge volume of deterministic problems are addressed and tackled by using the available sample data sets.  Because of this now a days machine learning concepts are extensively used in computer science and many other fields.  But still we need to explore more to implement machine learning in a specific field such as network analysis, stock trading, spam filters, traffic analysis, real-time and non-real time traffic etc., which may not be available in text books.  Here I would like to discourse some of the key points that the machine learning researchers and practitioners can make use of them.  These include shortcomings and concerns also.

     

     


  • References

    1. [1] J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. Byers. Big data: The nextfrontier for innovation, competition, and productivity. Technical report, McKinsey Global Institute, 2011.

      [2] Pedro Domingos, “A Few Useful Things to Know about Machine Learningâ€, Communications of the ACM, 2012.

      [3] http://insidebigdata.com/2013/12/12/tech-tip-generalization-machine-learning.

      [4] D. Wolpert. The lack of a priori distinctions between learning algorithms. Neural Computation,8:1341–1390, 1996.

      [5] M. Richardson and P. Domingos. Markov logic networks. Machine Learning, 62:107–136, 2006.

      [6] W. Cohen. Grammatically biased learning: Learning logic programs using an explicit antecedentdescription language. Artificial Intelligence, 68:303–366, 1994.

      [7] P. Domingos. A unified bias-variance decomposition and its applications. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 231–238, Stanford, CA, 2000. Morgan Kaufmann.

      [8] A. Y. Ng. Preventing “overfitting†of cross-validation data. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 245–253, Nashville, TN, 1997. Morgan Kaufmann.

      [9] Oommen, T. .; Misra, D. .; Twarakavi, N. K. C.; Prakash, A. .; Sahoo, B. .; Bandopadhyay, S. . (2008). "An Objective Analysis of Support Vector Machine Based


      [10] Classification for Remote Sensing". Mathematical Geosciences 40 (4): 409.

      [11] Thomas Oommen, Debasmita Misra, Navin K. C. Twarakavi, Anupma Prakash, Bhaskar Sahoo, Sukumar Bandopadhyay, An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing, Volume 40, Issue 4, pp 409-424,May 2008.

      [12] J. Tenenbaum, V. Silva, and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319–2323, 2000.

      [13] G. Hulten and P. Domingos. Mining complex models from arbitrarily large databases in constant time. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 525–531, Edmonton, Canada, 2002. ACM Press.

      [14] D. Kibler and P. Langley. Machine learning as an experimental science. In Proceedings of the Third European Working Session on Learning, London, UK, 1988. Pitman.

      [15] E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36:105–142, 1999.

      [16] R. Kohavi, R. Longbotham, D. Sommerfield, and R. Henne. Controlled experiments on the Web: Survey and practical guide. Data Mining and Knowledge Discovery, 18:140–181, 2009.

      [17] J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. Byers. Big data: The next frontier for innovation, competition, and productivity. Technical report, McKinsey Global Institute, 2011.

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  • How to Cite

    Chokkanathan, K., & Koteeswaran, S. (2018). A Study on Machine Learning: Elements, Characteristics and Algorithms. International Journal of Engineering & Technology, 7(2.19), 31-35. https://doi.org/10.14419/ijet.v7i2.19.13793

    Received date: 2018-06-07

    Accepted date: 2018-06-07

    Published date: 2018-04-17