A Case Study on Machine Learning Applications and Performance Improvement in Learning Algorithm

기계학습 응용 및 학습 알고리즘 성능 개선방안 사례연구

  • Received : 2015.12.24
  • Accepted : 2016.02.20
  • Published : 2016.02.28


This paper aims to present the way to bring about significant results through performance improvement of learning algorithm in the research applying to machine learning. Research papers showing the results from machine learning methods were collected as data for this case study. In addition, suitable machine learning methods for each field were selected and suggested in this paper. As a result, SVM for engineering, decision-making tree algorithm for medical science, and SVM for other fields showed their efficiency in terms of their frequent use cases and classification/prediction. By analyzing cases of machine learning application, general characterization of application plans is drawn. Machine learning application has three steps: (1) data collection; (2) data learning through algorithm; and (3) significance test on algorithm. Performance is improved in each step by combining algorithm. Ways of performance improvement are classified as multiple machine learning structure modeling, $+{\alpha}$ machine learning structure modeling, and so forth.


Supported by : 서울여자대학교 컴퓨터과학연구소


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