摘要
单球面分类器(RSS)以最大分离比为目标,对负类样本的分布情况缺乏考虑。根据Fisher判别准则,将相对间隔的思想引入到单球面分类器中,对特征空间中负类样本的分布上界进行约束来增强其内聚度,以提高分类器判别的准确性。由于分布上界的不可预测,为避免问题不可解,建立了自适应上界的最大相对分离比单球面分类器模型(ARRSS),并对模型参数进行了分析。实验证明,与单球面分类器相比,该方法表现出更好的泛化能力。
Without taking the spread of negative class samples into account,the objective of single spherical classifier（RSS）is only to maximize the separation ratio.According to the Fisher discriminant analysis,this paper introduced relative margin into RSS to enhance the cohesion of negative class samples and improve the discriminant accuracy by the upper bound constraint in the feature space.Because the upper bound is unpredictable,a maximum relative separation ratio single spherical classifier with an adaptive upper bound（ARRSS）was built to avoid no solution and its parameters were researched afterwards.Experiments show the proposed method achieves better generalization performance compared with RSS.
出处
《计算机科学》
CSCD
北大核心
2012年第9期188-191,214,共5页
Computer Science
基金
国家高新技术研究发展计划(863)项目(2009AA043503)
国家科技支撑计划项目(2012BAF10B05)资助
