Computational learning theory: an introduction
Computational learning theory: an introduction
Learning decision tree classifiers
ACM Computing Surveys (CSUR)
Principle of information diffusion
Fuzzy Sets and Systems
A comparative assessment of classification methods
Decision Support Systems
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
A frequency assessment expert system of piezoelectric transducers in paucity of data
Expert Systems with Applications: An International Journal
Identifying characteristics of seaports for environmental benchmarks based on meta-learning
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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In recent years, there has been a tremendous growth in the studies of the small data set learning methods in the condition of the paucity of data. Without double, information in data of small size is scarced and have some learning limit. As well as each classification method has its property. A method is the best solution for one data but is not the best for another. This article analyzes the characteristics of small data set learning. The Mega-fuzzification method for small data set learning is applied mainly. The comparison of different classification methods for small data set learning is also presented.