There are mainly two approaches for machine learning: symbolic and sub-symbolic. Decision tree is a typical model for symbolic learning, and neural network is a model for sub-symbolic learning. For pattern recognition, decision trees are more efficient than neural networks for two reasons. First, the computations in making decisions are simpler. Second, important features can be selected automatically during the design process. This paper introduces models for modular neural network that are a neural network tree where each node being an expert neural network and modular neural architecture where interconnections between modules are reduced. In this paper, we will study adaptation processes of neural network trees, modular neural network and conventional neural network. Then, we will compare all these adaptation processes during experimental work with the Fisher's Iris data set that is the bench test database from the area of machine learning. Experimental results with a recognition problem show that both models (e.g. neural network tree and modular neural network) have better adaptation results than conventional multilayer neural network architecture but the time complexity for trained neural network trees increases exponentially with the number of inputs.
展开▼
