支持向量机在仓储物害虫分类识别中的应用研究

仓储物害虫自动分类识别是储粮生态系统中的重要研究课题之一。近年来，支持向量机以其出色的学习性能，已成为机器学习领域的一个研究热点。本文在研究支持向量机理论的基础上，将支持向量机引入仓储物害虫的分类识别中。实验表明，该方法与传统的神经网络方法相比，克服了后者固有的过学习和欠学习问题，并且对复杂模式的识别能力较强，达到了较高的识别率，这为仓储物害虫的快速鉴定和分类研究开辟了新的途径。

Application Study of Support Vecter Machine in the Classification of Stored-Product Pests

The automatic classification of stored-product pests is an important research topic in the stored-grain ecosystem. Recently, Support Vector Machines (SVMs) has become a special topic in machine learning because of its outstanding learning capability. Based on studying its learning theory, Support Vector Machines is introduced in the classification of stored-product pests in this paper. It is concluded by experiments that this method overcomes the inherent defect of neural networks and achieves higher recognition rate. This technique has become a new vehicle to the automatic classification and identification of Stored-product Pests.