The Research of Support Vector Machine in Agricultural Data Classification

Abstract : The agricultural data classification is a hot topic in the field of precision agriculture. Support vector machine (SVM) is a kind of structural risk minimization based learning algorithms. As a popular machine learning algorithm, SVM has been widely used in many fields such as information retrieval and text classification in the last decade. In this paper, SVM is introduced to classify the agricultural data. An experimental evaluation of different methods is carried out on the public agricultural dataset. Experimental results show that the SVM algorithm outperforms two popular algorithms, i.e., naive bayes and artificial neural network in terms of the F1 measure.
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Communication dans un congrès
Daoliang Li; Yingyi Chen. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-370 (Part III), pp.265-269, 2012, Computer and Computing Technologies in Agriculture V. 〈10.1007/978-3-642-27275-2_29〉
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Lei Shi, Qiguo Duan, Xinming Ma, Mei Weng. The Research of Support Vector Machine in Agricultural Data Classification. Daoliang Li; Yingyi Chen. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-370 (Part III), pp.265-269, 2012, Computer and Computing Technologies in Agriculture V. 〈10.1007/978-3-642-27275-2_29〉. 〈hal-01361147〉

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