Machine learning
多层感知机 (MLP)
多层感知机 (Multilayer Perceptron, MLP) 是一种经典的全连接前馈神经网络，通过反向传播算法进行训练，该算法由 Rumelhart, Hinton & Williams 在其 1986 年发表于《自然》杂志的里程碑式论文中正式提出。MLP 由输入层、一个或多个神经元隐藏层以及输出层组成，能够学习输入特征到目标输出的非线性映射，并作为现代深度学习的基础构建模块。
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来源
- Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. DOI: 10.1038/323533a0 ↗
- Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning (Ch. 6–8). MIT Press. ISBN: 978-0-262-03561-3
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 5). Springer. ISBN: 978-0-387-31073-2
如何引用本页
ScholarGate. (2026, June 3). Multilayer Perceptron (Fully Connected Feedforward Neural Network). ScholarGate. https://scholargate.app/zh/deep-learning/multilayer-perceptron
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