摘要
为了对目标进行快速的检测,提出了一种新的基于支持向量机的级联式分类器的构造方法。该级联分类器由若干个线性SVM弱分类器构成,结构简单,分类时间极快。针对级联结构中的每个节点的训练给出了一个新的SVM框架下的二次规划模型,这使得每个节点都有较高的正样本检测率和适当的负样本错检率。实际的实验结果表明,与经典非线性SVM分类器相比,这种分类器在保持SVM较强泛化性能的优点的同时,在检测效率方面更是具有明显的优势。
To detect objects quickly,a new method is presented to construct a cascade of SVM classifiers. The classifier which contains several weak linear SVM classifiers is simple to understand and is extremely efficient. The learning problem of every node in the cascade structure is described as a new quadratic programming problem in the framework of SVM, which makes every linear classifier achieve very high detection rate but only moderate false positive rate. The real experiments show that this method enjoys good generalization capacity and much fast speed compared with the traditional SVMs.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第14期39-41,53,共4页
Computer Engineering and Applications
基金
国家自然科学基金重大项目(the Grand National Natural Science Foundation of China under Grant No.60234030)
作者简介
安平（1984-），女，硕士研究生，研究方向为机器学习；
吴涛（1975-），男，副教授，研究方向为机器学习与图像处理；
贺汉根（1943-），男，教授，博士生导师，研究方向为智能系统，
