Wavelet transformation and hidden Markov model are used in wavelet-based HMT model for analyzing andprocessing images. Expected Maximization(EM) algorithm used in training model results in slow convergence. Thepersistence, exponential decay characteristics of wavelet coefficient are analyzed. A model parameter initializationmethod is proposed. This method provides reasonable initial model value, reduces training time greatly. Its applica-tion in image de-noising demonstrates is validity.
DM usually means an efficient knowledge discovery from database, and the immune algorithm is a biological theory-based and global searching algorithm. A novel induction algorithm is proposed here which integrates a power of individual immunity and an evolutionary mechanism of population. This algorithm does not take great care of discovering some classifying information, but unknown knowledge or a predication on higher level rules. Theoretical analysis and simulations both show that this algorithm is prone to the stabilization of a population and the improvement of entire capability, and also keeping a high degree of preciseness during the rule induction.
