Titlebook: Advances in Neural Networks - ISNN 2006; Third International Jun Wang,Zhang Yi,Hujun Yin Conference proceedings 2006 Springer-Verlag Berli

骯臟

Ying Tong,Kaikai Li,Jin Chen,Rong Liufication because it often lead the NN to overcompensate for the dominant group. Therefore, in this paper a dynamic threshold learning algorithm (DTLA) is proposed as the substitute for the conventional LSE algorithm. This method uses multiple dynamic threshold parameters to gradually remove some tra

令人悲傷

無(wú)力更進(jìn)

CHASE

https://doi.org/10.1007/978-1-4614-5803-6ecognition are extracted from the segmented iris pattern using two-dimensional (2-D) wavelet transform based on Haar wavelet. We present an efficient initialization method of the weight vectors and a new method to determine the winner in LVQ neural network. The proposed methods have more accuracy th

使服水土

Schlemms-Canal

完全

addition

Lei Dai,Liwei Zhang,Limin Li,You Chenh facial expression, what are the patterns that distinguish them from one another. We applied widely used pattern recognition technique-principle component analysis to characterize the feature point displacements of each basic human facial expression for each individual in the existing database. For

ACRID

暗諷

Yongkui Ma,Shaopeng Zhang,Jiayan Zhangnition task. Given that AAM has been also used in tracking the moving object, we thought it could be effective in recognizing the facial expressions of humans. Our results show that the performance of the facial expression recognition using AAM is reliably high when it combined with an enhanced Fish