摘要
鉴于学习样本的选择对神经网络的泛化能力有很大影响,本文提出学习样本的选择应针对被逼近的非线性对象的特性,采用均匀设计法构造样本中心,结合聚类理论对学习样本进行优选。应用结果表明这种方法可以提高神经网络的泛化能力。
Learning sample selection has the very tremendous influence to the generalization ability of the neural network. This paper proposed that the learning sample selection should consider the characteristics of the non-linear object approached. A method of selecting learning samples based on the uniform design and clustering theory is discussed. The result of application shows that the effective selection of the learning samples can improve the generalization ability of the neural networks.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2005年第2期252-255,共4页
Pattern Recognition and Artificial Intelligence
关键词
径向基函数
神经网络
均匀设计
样本选择
聚类理论
泛化能力
Radial Basis Function (RBF)
Neural Network
Uniform Design
Sample Selection
Clustering Theory
Generalization Ability
