Some Salient Aspects of Machine Learning Research: A Bibliometric Analysis

Abstract

Machine learning has emerged as an important and distinct area of research closely related to and often overlaps with various domains within computer science, computational statistics, artificial intelligence, cognitive science. One can observe connections with these fields at the cognitive level (in terms of theoretical framework), and on methodological levels (drawing from tools and techniques of these fields). The evolution of the field has taken a very directed and operational approach with basic tenet of machine learning being ‘teaching computers how to learn from data to make decisions or predictions’. As we move into systems that increasingly need to exploit data, we find the research in this area getting more application oriented, expansive in scope with loci of research and innovation dispersed across academia, research institutions and industry. It is thus becoming a challenging as well as useful exercise to know the structure and dynamics of this field. The paper is centered on this issue; it tries to capture the intellectual structure of this field and research trends from quantitative and statistical analysis of research publications. Conceptual connections are constructed from linkages among keywords using tools and techniques of Social network Analysis. It also acts as a conceptual framework for the study. Some indications from patent statistics are also drawn to provide some insights of the technological trends.