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http://dx.doi.org/10.14400/JDC.2018.16.10.205

A study on the standardization strategy for building of learning data set for machine learning applications  

Choi, JungYul (Division of Computer Engineering, Sungkyul University)
Publication Information
Journal of Digital Convergence / v.16, no.10, 2018 , pp. 205-212 More about this Journal
Abstract
With the development of high performance CPU / GPU, artificial intelligence algorithms such as deep neural networks, and a large amount of data, machine learning has been extended to various applications. In particular, a large amount of data collected from the Internet of Things, social network services, web pages, and public data is accelerating the use of machine learning. Learning data sets for machine learning exist in various formats according to application fields and data types, and thus it is difficult to effectively process data and apply them to machine learning. Therefore, this paper studied a method for building a learning data set for machine learning in accordance with standardized procedures. This paper first analyzes the requirement of learning data set according to problem types and data types. Based on the analysis, this paper presents the reference model to build learning data set for machine learning applications. This paper presents the target standardization organization and a standard development strategy for building learning data set.
Keywords
Machine learning; Artificial Intelligence; Reference model; Standardization; Learning data set;
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