Acquiring knowledge with limited experience

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

Research output: Contribution to journalArticlepeer-review

8 Citations (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.

Original languageEnglish
Pages (from-to)162-170
Number of pages9
JournalExpert Systems
Issue number3
Publication statusPublished - 2007 Jul

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Acquiring knowledge with limited experience'. Together they form a unique fingerprint.

Cite this