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

11 Citations (Scopus)

Abstract

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
Volume24
Issue number3
DOIs
Publication statusPublished - 2007 Jul

All Science Journal Classification (ASJC) codes

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

Fingerprint

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

Cite this