The Characteristics of Learning in Limited Data and the Comparative Assessment of Learning Methods
نویسنده
چکیده
Many studies about learning in limited data were made in recent years. Without double, small data set learning is a challenging problem. Information in data of small size is scarce and has some learning limit. While discussing the learning accuracy in limited data, different classification method causes different results for different data because each classification method has its property. A method is the best solution for one data but is not the best for another. Therefore, this study analyzes the characteristics of small data set learning by the comparison of classification methods. The Mega-fuzzification method for small data set learning is applied mainly. The comparison of different classification methods for small data set learning with several kinds of data is also presented. Key-Words: Small data set, Mega-fuzzification, Machine learning, Classification method, Paucity of data
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