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同行評審

11 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)162-170
期刊Expert Systems
出版狀態Published - 2007 7月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 理論電腦科學
  • 計算機理論與數學
  • 人工智慧


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