Machine learning
多层感知机 (MLP)
多层感知机（MLP）是一种前馈神经网络架构，通过反向传播进行训练，由 Rumelhart、Hinton 和 Williams 在他们 1986 年的开创性《自然》论文中正式提出。MLP 由输入层、一个或多个具有非线性激活函数的神经元隐藏层以及输出层组成，能够以任意精度近似任何连续函数，并作为经典机器学习与现代深度学习之间的概念桥梁。
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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. 6–7). 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). Multi-layer Perceptron (Feedforward Neural Network with Backpropagation). ScholarGate. https://scholargate.app/zh/machine-learning/multi-layer-perceptron
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