Distance Measures for Nonparametric Weak Process Models
Yong Hua Sheng, George V. Cybenko · 2006
Nonparametric versions of hidden Markov models, what we call weak models, are robust for process detection and easy to construct, as the assumption of knowing precise probabilities in HMMs is weakened to {0,1}-values of reachabilities. Weak models are shown to be equivalent to DFAs/ NFAs. The concept of minimal unifilar weak model (/spl mu/-WM) is introduced. The spectral radius of the transition matrix of /spl mu/-WM determines the growth rate of acceptable observation sequences. An absolute weak model distance is defined for model clustering purpose, while a relative distance is a measure of how fast the performance of detection gets improved as more observations arrive. Convergence of the distance measures is proved.