alexa Machine-impact-factor| OMICS International | Journal Of Applied Mechanical Engineering

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Machine Impact Factor

Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems. The journal features papers that describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems or methods provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. The impact factor of journal provides quantitative assessment tool for grading, evaluating, sorting and comparing journals of similar kind. It reflects the average number of citations to recent articles published in science and social science journals in a particular year or period, and is frequently used as a proxy for the relative importance of a journal within its field. It is first devised by Eugene Garfield, the founder of the Institute for Scientific Information. The impact factor of a journal is evaluated by dividing the number of current year citations to the source items published in that journal during the previous two years.
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Last date updated on September, 2014

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