Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [16 references]  Display  Hide  Download

https://hal.inria.fr/hal-01361147
Contributor : Hal Ifip <>
Submitted on : Tuesday, September 6, 2016 - 5:09:01 PM
Last modification on : Thursday, April 11, 2019 - 10:42:06 AM
Long-term archiving on: : Wednesday, December 7, 2016 - 1:26:50 PM

File

978-3-642-27275-2_29_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Lei Shi, Qiguo Duan, Xinming Ma, Mei Weng. The Research of Support Vector Machine in Agricultural Data Classification. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. pp.265-269, ⟨10.1007/978-3-642-27275-2_29⟩. ⟨hal-01361147⟩

Share

Metrics

Record views

315

Files downloads

562