Abstract
Machine learning techniques are useful for solving different problems in many applications. Different machine learning techniques are available for assessment and forecasting of data. For illustration, this paper is focused alight on four machine learning techniques that are Weka, Tanagra, R software and MATLAB for showing different views to analyze and forecast the data. Weka is the most effective machine learning technique for regression and classification problems. Tanagra, the data mining tool, which is a supervised learning technique and also suitable for statistical analysis, classification and clustering problems. R software is a flexible programming accent for statistical computing and graphical settings. Finally, MATLAB is exclusive for technical computing representing the data in 2D, 3D and it is very effective tool for predictive analysis.
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References
Dutton, D., Conroy, G.: A review of machine learning, knowledge engineering. Knowl. Eng. Rev. 12(14), 341–367 (December 1997)
De Mantaras, Armengol, E.: Machine learning from examples: inductive and Lazy methods. Data Knowl. Eng. 99–123 (1998)
Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Ian, H.: WEKA A Machine Learning Workbench for Data Mining
Ramamohan, Y., Vasantharao, K., Kalyana Chakravarti, C., Ratnam, A.S.K.: A study of data mining tools in knowledge discovery process. Int. J. Soft Comput. Eng. (IJSCE) 2(3), (July 2012). ISSN:2231-2307
Smith, G., Whitehead, J., Mateas, M.: Tanagra: A Mixed-Initiative Level Design Tool
Devika, A.S., Gupta, M.: Case study on classification of glass using neural network tool in MATLAB. In: International Conference on Advances in Computer Engineering & Applications (ICACEA-2014) at IMSEC, GZB 11
Demuth, H., Beale, M.: Neural Network Toolbox for Use with MATLAB
Kaundal, R., Kapoor, A.S., Raghava, G.P.S.: Machine learning techniques in disease forecasting: a case study on rice blast prediction. In: BMC Bioinformatics Article, 1–16, Nov 3 2006
Kotsiantis, S., et al.: Data preprocessing for supervised leaning. Int. J. Comput. Sci. 1(2), 111–117 (2006)
http://www.statisticalconsultants.co.nz/blog/tanagra-a-free-data-mining-program.html
Camargo, L.S., Yoneyama, T.: Specification of training sets and the number of hidden neurons for multilayer perceptrons. Neural Comput. 2673–2680 (2001)
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Vidyullatha, P., Rao, D.R., Prasanth, Y., Changala, R., Narayana, L. (2016). Integrating Different Machine Learning Techniques for Assessment and Forecasting of Data. In: Shetty, N., Prasad, N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2553-9_12
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DOI: https://doi.org/10.1007/978-81-322-2553-9_12
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