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Improvised number identification using SVM and random forest classifiers

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References

  • T. K. Ho , “Random decision forests,” in Proceedings of 3rd international conference on document analysis and recognition , 1995, vol. 1, pp. 278–282. doi: 10.1109/ICDAR.1995.598994
  • A. Ben-Hur , D. Horn , H. T. Siegelmann , and V. Vapnik , “Support vector clustering,” J. Mach. Learn. Res ., vol. 2, no. Dec, pp. 125–137, 2001.
  • P. Thanh Noi and M. Kappas , “Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery,” Sensors , vol. 18, no. 1, p. 18, 2018.
  • E. Raczko and B. Zagajewski , “Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images,” Eur. J. Remote Sens ., vol. 50, no. 1, pp. 144–154, Jan. 2017. doi: 10.1080/22797254.2017.1299557
  • I. Bin Mohamad and D. Usman , “Standardization and its effects on K-means clustering algorithm,” Res. J. Appl. Sci. Eng. Technol ., vol. 6, no. 17, pp. 3299–3303, 2013. doi: 10.19026/rjaset.6.3638
  • Vanita Jain , Rishabh Kapoor , Shashwat Gulyani & Arun Kumar Dubey (2019) Categorization of spam images and identification of controversial images on mobile phones using machine learning and predictive learning, Journal of Discrete Mathematical Sciences and Cryptography , 22:2, 293-307 doi: 10.1080/09720529.2019.1582863
  • R. Chen , T. Chen , Y. Chien , and Y. Yang , “Novel questionnaire-responded transaction approach with SVM for credit card fraud detection,” in International Symposium on Neural Networks , 2005, pp. 916– 921.
  • Cheng-Chi Lee , Hsien-Ju Ko & Shun-Der Chen (2012) An improved simple user authentication scheme for grid computing, Journal of Discrete Mathematical Sciences and Cryptography , 15:2-3, 113-124 doi: 10.1080/09720529.2012.10698368
  • S. Amini , S. Homayouni , A. Safari , and A. A. Darvishsefat , “Object-based classification of hyperspectral data using Random Forest algorithm,” Geo-spatial Inf. Sci ., vol. 21, no. 2, pp. 127–138, 2018. doi: 10.1080/10095020.2017.1399674
  • A. O. Ok , O. Akar , and O. Gungor , “Evaluation of random forest method for agricultural crop classification,” Eur. J. Remote Sens ., vol. 45, no. 1, pp. 421–432, 2012. doi: 10.5721/EuJRS20124535

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