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Tan, C. and Li, M.L. (2008) Mutual Information-Induced Interval Selection Combined with Kernel Partial Least Squares for Near-Infrared Spectral Calibration. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 71, 1266-1273. http://dx.doi.org/10.1016/j.saa.2008.03.033

has been cited by the following article:

  • TITLE: Mutual Information-Based Modified Randomized Weights Neural Networks

    AUTHORS: Jian Tang, Zhiwei Wu, Meiying Jia, Zhuo Liu

    KEYWORDS: Randomized Weights Neural Networks, Mutual Information, Feature Selection

    JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.11, November 19, 2015

    ABSTRACT: 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.