The aim of this work is to develop a new method to overcome the increased training time when a recognition model is updated\nbased on the condition of new features extracted from new samples. As a common complex system, red wine has a rich\nchemical composition and is used as an object of this research. The novel method based on incremental learning support vector\nmachine (I-SVM) combined with ultravioletââ?¬â??visible (UV-Vis) spectroscopy was applied to discriminant analysis of the brands\nof red wine for the first time. In this method, new features included in the new training samples were introduced into the\nrecognition model through iterative learning in each iteration, and the recognition model was rapidly updated without\nsignificantly increasing the training time. Experimental results show that the recognition model established by this method\nobtains a good balance between training efficiency and recognition accuracy.
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