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Empirical studies on the online learning algorithms based on combining weight noise injection and weight decay
|關鍵字:||Neural Networks, Multilayer Perceptron (MLP);類神經網路;Fault Tolerance;Weight Decay;Weight Noise;多層感知器;容錯;權重衰減;權重雜訊||出版社:||科技管理研究所||引用:|| G. An. The effects of adding noise during backpropagation training on a generalization performance. Neural Computation, Vol.8, 643-674, 1996.  G. Basalyga and E. Salinas. When response variability increases neural network robustness to synaptic noise. Neural Computation, Vol.18, 1349-1379, 2006.  J. L. Bernier, J. Ortega, E. Ros I. Rojas, and A. Prieto. Obtaining fault tolerance multilayer perceptrons using an explicit regularization. Neural Processing Letters, Vol.12, 107-113, 2000.  J. L. Bernier, J. Ortega, I. Rojas, and A. Prieto. Improving the tolerance of multilayer perceptrons by minimizing the statistical sensitivity to weight deviations. Neurocomputing, vol.31, pp.87-103, Jan. 2000.  G. Bolt. Fault tolerant in multi-layer perceptrons. PhD Thesis, University of York, UK,, 1992.  Massimo Buscema. Metanet: The theory of independent judges. 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在類神經網路中, 為了改善容錯效果而在訓練時加入權重雜訊(weight noise) 已經被廣泛的採用, 但是在理論上及實證上皆未獲得證實。在本論文中, 我們將從兩個方面來討論-在weight noise) 和權重衰減(weight decay)。我們把multiplicative weight noise 及additive weight noise 兩種情況分開探討。為了證實收斂情況和容錯能力的表現, 我們透過大量的電腦模擬來得到所需的結果。實驗結果顯示:(一) 在訓練時加入權重雜訊(weight noise) 將不會使權重收斂。(二) 同時加入權重雜訊(weight noise) 和權重衰減(weight decay) 的收斂情況較只加入權重雜訊(weight noise) 來得更好。(三)同時加入權重雜訊(weight noise) 和權重衰減(weight decay) 的容錯能力較只加入權重雜訊(weight noise) 來得更好。
本論文有以下兩個貢獻: 第一, 這些研究結果的一部分, 補充了在最近由Ho, Leung & Sum 三人在訓練時加入權重雜訊(weight noise) 的收斂情況的研究結果。第二, 另外一部分結果關於容錯的部分在類神經網路裡是一個新的領域。最後, 本論文也帶出一個在訓練時加入權重衰減(weight decay) 的重要訊息。加入權重衰減(weight decay) 不僅可以提高權重的收斂, 也可以改善類神經網路的容錯效果。
While injecting weight noise during training have been widely adopted in attaining fault tolerant neural newtorks, theoretical and empirical studies on the online algorithms developed based on these strategies have yet to be complete. In this thesis, we will investigate two important aspects in regard to the online learning algorithms based on combining weight noise injection and weight decay. Multiplicative weight noise and additive weight noise are considered seperately. The convergence behaviors and the performance of those learning algorithms are investigated via intensive computer simulations. It is found that (i) the online learning algorithm based on purely multiplicative weight noise injection does not converge, (ii) the algorithms combining weight noise injection and weight decay exhibit better convergence behaviors than their pure weight noise injection counterparts, and (iii) the neural networks attained by these algorithms combining weight noise injection and weight decay showing better fault tolerance abilities than the neural networks attained by the pure weight noise injection-based algorithms.
The contributions of these results are two folds. First, part of these empirical results complement the recent findings from Ho, Leung & Sum on the convergence behaviors of the weight noise injection-based learning algorithms. Second, another part of the results which is in regard to the fault tolerance ability are new in the area. Finally, one should note that the results presented in this thesis also bring out an important message adding weight decay during training. Weight decay is not just can improve the convergence of an algorithm, but also can improve the weight noise tolerance ability of a neural network that is attained by these online algorithms.
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