6.3 Synaptic convergence to centroids:AVQ Algorithms Unsupervised Competitive Learning (UCL) m,(t+1)=m,(t)+c,[x(t)-m(t)] m,(t+1)=,(t) fi≠j {c,}defines a slowly decreasing sequence of learning coefficient For inatane.0.000 for 10,000 samples x() Supervised Competitive Learning (SCL) m,(t+1)=m,()+c(x()x(0)-m,()] m,(t)+c,[x()-m,(t】fx∈D, m,(t)-c,[x(t)-m,(t)]if xD 2003.11.19 112003.11.19 11 6.3 Synaptic convergence to centroids: AVQ Algorithms Unsupervised Competitive Learning (UCL) ( 1) ( ) [ ( ) ( )] ( 1) ( ) j j j t i i m t m t c x t m t m t m t if i j + = + − + =  { }t c defines a slowly decreasing sequence of learning coefficient For instance , 0.1 1 for 10,000 samples ( ) 10,000 t t c x t   = −     Supervised Competitive Learning (SCL) ( 1) ( ) ( ( )) ( ) ( ) ( ) [ ( ) ( )] ( ) [ ( ) ( )] j j t j j j t j j j t j j m t m t c r x t x t m t m t c x t m t if x D m t c x t m t if x D + = + −      + −  =   − −  