In this paper, we investigate and analyze the weight distribution of feedforward two-layer neural networks in order to understand and improve the time-consuming training process of neural networks. Generally, it takes a long time to train neural networks. However, when a new problem is presented, neural networks have to be trained again without any benefit from previous training. In order to address this problem, we view training process as finding a solution weight point in a weight space and analyze the distribution of solution weight points in the weight space. Then, we propose a weight initialization method that uses the information on the distribution of the solution weight points. Experimental results show that the proposed weight initialization method provides a better performance than the conventional method that uses a random generator in terms of convergence speed. © Springer-Verlag 2004.
CITATION STYLE
Go, J., Baek, B., & Lee, C. (2004). Analyzing weight distribution of feedforward neural networks and efficient weight initialization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3138, 840–849. https://doi.org/10.1007/978-3-540-27868-9_92
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