Indian Tea Discriminator: SVM Approach
نویسندگان
چکیده
Artificial Organoleptic Systems are being used today for a variety of detection tasks from quality control of food products to medical diagnosis. The optimization of sample preparation, signal processing, feature extraction, classifier are as important as choice of sensors within the array in enhancing the performance of the organoleptic system. It is difficult to determine if all features considered are necessary for the classifier while classifying megavariate data. The presence of irrelevant features increases the dimensionality of the search space, which can potentially deluge the accuracy of the Pattern Recognition (PARC) techniques. Hence, a systematic method is required to reduce the number of features in order to optimize the performance of PARC. Tea in present time is the most popular beverages having huge global marketing. It is a very complex chemical compound graded by various testers' score,
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