Computational Statistics and Predictive Analysis in Machine Learning

Deepa H. Kulkarni

Abstract: Machine learning evolved from the study of pattern recognition and computational learning theory in intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions. Machine learning is closely related to and often overlaps with computational statistics, a discipline that also specializes in prediction-making. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition can be viewed as two facets of the same field. When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive analytics

Keywords: computational learning, computational statistics, predictive analytics

How to Cite?: Deepa H. Kulkarni, "Computational Statistics and Predictive Analysis in Machine Learning", Volume 5 Issue 1, January 2016, International Journal of Science and Research (IJSR), Pages: 1521-1524, https://www.ijsr.net/getabstract.php?paperid=NOV152818, DOI: https://dx.doi.org/10.21275/NOV152818

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