An extendable framework is developed for ensemble HME model based on the theory analysis of information geometry. In a hierarchical set of systems, a lower order system is included in the parameter space of a large one as a subset. Such a parameter space has rich geometrical structures that are responsible for the dynamic behaviors of learning. HME network divides a task into small tasks by the principle of divide and conquer to improve the performance of a single network. By studying the dual manifold architecture for mixtures of neural networks and analyzing the probability of Knowledge-increasable Model based on information geometry, the paper proposes a new method to achieve the multi-HME model that has knowledge-increasable and structure-extendible ability. The method helps to provide explanation of the transformation mechanism of human recognition system and understand the theory of global architecture of neural network.
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