یک الگوریتم یادگیری تکراری برای شبکه های عصبی پیشخور با وزن های تصادفی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|41516||2016||12 صفحه PDF||سفارش دهید||5860 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Information Sciences, Volume 328, 20 January 2016, Pages 546–557
Feedforward neural networks with random weights (FNNRWs), as random basis function approximators, have received considerable attention due to their potential applications in dealing with large scale datasets. Special characteristics of such a learner model come from weights specification, that is, the input weights and biases are randomly assigned and the output weights can be analytically evaluated by a Moore–Penrose generalized inverse of the hidden output matrix. When the size of data samples becomes very large, such a learning scheme is infeasible for problem solving. This paper aims to develop an iterative solution for training FNNRWs with large scale datasets, where a regularization model is employed to potentially produce a learner model with improved generalization capability. Theoretical results on the convergence and stability of the proposed learning algorithm are established. Experiments on some UCI benchmark datasets and a face recognition dataset are carried out, and the results and comparisons indicate the applicability and effectiveness of our proposed learning algorithm for dealing with large scale datasets.