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
Related Articles
- EP ID EP687858
- DOI 10.14704/WEB/V17I1/WEB17011
- Views 215
- Downloads 0
Bibliometric analysis of output and visibility of science and technology in Singapore during 2000- 2009
The main focal point of this paper lies on the appraisal of rising national scientific output in Singapore with the help of bibliometric indicators. This study presents the analysis of 83,439 papers that were published i...
Organising Development Knowledge: Towards Situated Classification Work on the Web
This paper addresses the classification of development knowledge in web-based resources. Seven categories of a marginalised knowledge domain are mapped across eleven web resources, with additional observations of classif...
Tell Me Why Bob Dylan and the Beatles Song Titles Are Used in Biomedical Literature
How often and why do scientists refer to music titles in their papers? There has been a growing trend of using popular music titles in scientific literature since the 1990s. We have investigated the extent to which songs...
An Algorithm for Classification, Localization and Selection of Informative Features in the Space of Politypic Data
Dimensionality reduction and feature subset selection are very important and challenging issues in preliminary processing of the large amount of data for its intellectual analysis, pattern recognition and clustering. In...
Global Information Inequalities: Bridging the information gap
Digital divide has been one of the most important issues being discussed among information professionals all over the world during the past decade. Traditionally, political systems, public and private sectors, government...