In this paper, the dairy cattle movement detecting technology based on 3-axis acceleration sensor information fusion is presented. For they show ideal performance in generalization and optimization, Support vector machines are used to build an information fusion model for dairy cattle's behavior classification. The data feature of the support vector machine fusion model is derived from 3-axis acceleration data. RBF function is used as the model's kernel function. The genetic algorithm is used to optimize the parameters of the kernel function. The training and testing results show that using genetic algorithm for kernel function parameter searching has good ability to optimize the fusion model. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.
CITATION STYLE
Zhou, H. E., Yin, L., & Liu, C. X. (2012). Dairy cattle movement detecting technology using support vector machine. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (Vol. 72 LNICST, pp. 23–32). https://doi.org/10.1007/978-3-642-29157-9_3
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