A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms
Journal Title: Webology - Year 2020, Vol 17, Issue 1
Abstract
With the explosion of data in recent times, Machine learning has emerged as one of the most important methodical approaches to observe significant insights from the vast amount of data. Particularly, it is witnessed that with the alarming rise in the volume of unstructured data on the world wide web, machine learning algorithms can be applied in a wide number of domains to solve various problems related to understanding humans. At the onset, this paper introduces the field of machine learning, classic learning approaches, and machine learning algorithms. A theoretical comparison study of state of the art algorithms is carried based on their logic, characteristics, weaknesses, strengths, and kind of applications in which these algorithms can be used. The study is expected to help buddy researchers who are in the beginning to work in this area.
Authors and Affiliations
Shailja Gupta, Manpreet Kaur, Sachin Lakra and Yogesh Dixit
Keywords
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- EP ID EP687858
- DOI 10.14704/WEB/V17I1/WEB17011
- Views 214
- Downloads 0
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