方法对比

	多层感知机 (MLP)	受限玻尔tzmann机 (RBM)
领域	深度学习	深度学习
方法族	Machine learning	Latent structure
起源年份	1986	1986
提出者	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.	Smolensky, P. (1986); popularised by Hinton, G. E. & Salakhutdinov, R. R. (2006)
类型	Supervised feedforward neural network	Generative energy-based probabilistic model
开创性文献	Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.	Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.
别名	MLP, feedforward neural network, fully connected neural network, vanilla neural network	RBM, Harmonium, restricted Boltzmann machine, RBM generative model
相关	4	3
摘要	A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning.	A Restricted Boltzmann Machine is a two-layer generative probabilistic model consisting of visible (observed) and hidden (latent) binary units connected by an undirected bipartite graph with no within-layer connections. Originally introduced as the 'Harmonium' by Paul Smolensky in 1986 and powerfully revived by Geoffrey Hinton and Ruslan Salakhutdinov in their landmark 2006 Science paper, RBMs became historically pivotal as the building block for greedy layer-wise pre-training of Deep Belief Networks, restarting interest in deep neural networks after years of stagnation.