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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.
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Keywords: artificial intelligence; decision tree; mega-trend diffusion; neural network; small-data-set learning; support vector machine

Document Type: Research Article

Affiliations: 1: Department of Industrial and Information Management, National Cheng Kung University, 1 University Road, Tainan 701, Taiwan 2: Department of Public Health, College of Medicine, National Cheng Kung University, Tainan, Taiwan, Email: [email protected]

Publication date: July 1, 2007

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