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http://dx.doi.org/10.13067/JKIECS.2019.14.4.701

Performance Comparison Analysis of AI Supervised Learning Methods of Tensorflow and Scikit-Learn in the Writing Digit Data  

Jo, Jun-Mo (Dept. Electronic Engineering, TongMyong University)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.14, no.4, 2019 , pp. 701-706 More about this Journal
Abstract
The advent of the AI(: Artificial Intelligence) has applied to many industrial and general applications have havingact on our lives these days. Various types of machine learning methods are supported in this field. The supervised learning method of the machine learning has features and targets as an input in the learning process. There are many supervised learning methods as well and their performance varies depends on the characteristics and states of the big data type as an input data. Therefore, in this paper, in order to compare the performance of the various supervised learning method with a specific big data set, the supervised learning methods supported in the Tensorflow and the Sckit-Learn are simulated and analyzed in the Jupyter Notebook environment with python.
Keywords
AI; Machine Learning; Supervised Learning; Tensorflow; Performance Evaluation;
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Times Cited By KSCI : 5  (Citation Analysis)
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