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In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. When trained on a set of examples in an unsupervised way, a DBN can learn to probabilistically reconstruct its inputs. The layers then act as feature detectors on inputs.〔 After this learning step, a DBN can be further trained in a supervised way to perform classification.〔 DBNs can be viewed as a composition of simple, unsupervised networks such as restricted Boltzmann machines (RBMs)〔 or autoencoders, where each sub-network's hidden layer serves as the visible layer for the next. This also leads to a fast, layer-by-layer unsupervised training procedure, where contrastive divergence is applied to each sub-network in turn, starting from the "lowest" pair of layers (the lowest visible layer being a training set). The observation, due to Yee-Whye Teh, Geoffrey Hinton's student, that DBNs can be trained greedily, one layer at a time, has been called a breakthrough in deep learning. ==Training algorithm== The training algorithm for DBNs proceeds as follows.〔 Let be a matrix of inputs, regarded as a set of feature vectors. # Train a restricted Boltzmann machine on to obtain its weight matrix, . Use this as the weight matrix between the lower two layers of the network. # Transform by the RBM to produce new data , either by sampling or by computing the mean activation of the hidden units. # Repeat this procedure with ← for the next pair of layers, until the top two layers of the network are reached. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Deep belief network」の詳細全文を読む スポンサード リンク
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