Acquiring knowledge with limited experience

Der Chiang Li, Chun Wu Yeh, Tung I. Tsai, Yao Hwei Fang, Susan C. Hu

研究成果: Article

11 引文 斯高帕斯(Scopus)

摘要

From computational learning theory, sample size in machine learning problems indeed affects the learning performance. Since only few samples can be obtained in the early stages of a system and fewer exemplars usually lead to a low learning accuracy, this research compares different machine learning methods through their classification accuracies to improve small-data-set learning. Techniques used in this paper include the mega-trend diffusion technique, a backpropagation neural network, a support vector machine, and decision trees to explore the machine learning issue with two real medical data sets concerning cancer. The result of the experiment shows that the mega-trend diffusion technique and backpropagation approaches are effective methods of small-data-set learning.

原文English
頁(從 - 到)162-170
頁數9
期刊Expert Systems
24
發行號3
DOIs
出版狀態Published - 2007 七月 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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