Special Issue on Machine Learning and Its Application

Machine learning is an application of artificial intelligence that automates analytical model building by using algorithms that iteratively learn from data without being explicitly programmed where to look. It constitutes subfield of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed." Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data– such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible.

In this special issue, we intend to invite front-line researchers and authors to submit original research and review articles on machine learning and its application. Potential topics include, but are not limited to:

• Decision trees and machine learning
• Genetic algorithms and machine learning
• Machine learning for information extraction
• Gaussian processes for machine learning
• Artificial intelligence approach
• Machine learning, neural and statistical classification
• Selection of features in machine learning
• Bayesian learning and vector machine
• Machine learning tools and techniques

Authors should read over the journal’s For Authors carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

Please kindly notice that the “Special Issue” under your manuscript title is supposed to be specified and the research field “Special Issue – Machine Learning and Its Application” should be chosen during your submission.

According to the following timetable:

JSEA Editorial Office