The Research of Support Vector Machine in Agricultural Data Classification View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Lei Shi , Qiguo Duan , Xinming Ma , Mei Weng

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 F 1 measure. More... »

PAGES

265-269

References to SciGraph publications

Book

TITLE

Computer and Computing Technologies in Agriculture V

ISBN

978-3-642-27274-5
978-3-642-27275-2

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-27275-2_29

DOI

http://dx.doi.org/10.1007/978-3-642-27275-2_29

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033429167


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