Mutual Information-Based Modified Randomized Weights Neural Networks

• 作者: Jian Tang Meiying Jia Zhiwei Wu Zhuo Liu
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: RANDOMIZED WEIGHTS NEURAL Networks Mutual Information FEATURE Selection
• 年，卷（期）: dnhtxyw_2015,（11）
• 页码范围: 191-197
• 页数: 7页
• 分类号: TP39

摘要

Randomized weights neural networks have fast learning speed and good generalization performance with one single hidden layer structure. Input weighs of the hidden layer are produced randomly. By employing certain activation function, outputs of the hidden layer are calculated with some randomization. Output weights are computed using pseudo inverse. Mutual information can be used to measure mutual dependence of two variables quantitatively based on the probability theory. In this paper, these hidden layer’s outputs that relate to prediction variable closely are selected with the simple mutual information based feature selection method. These hidden nodes with high mutual information values are maintained as a new hidden layer. Thus, the size of the hidden layer is reduced. The new hidden layer’s output weights are learned with the pseudo inverse method. The proposed method is compared with the original randomized algorithms using concrete compressive strength benchmark dataset.