Comparative analysis of the EFuNN and the Support Vector Machine models for the classification of horticulture data
نویسنده
چکیده
Support Vector Machines (SVM) over the past six years have emerged as one of the more robust classification architectures coupled with good generalisation capabilities. Similarly the Evolving Fuzzy Neural Network (EFuNN) have also been reported to posses these properties as well. This paper compares these two models and then applies them to the classification of two datasets taken from the horticulture industry. The results that are reported indicate that the EFuNN performs comparably well against its statistically based counterpart.
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