A Practical Guide to Training Restricted Boltzmann Machines

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文件名称：A Practical Guide to Training Restricted Boltzmann Machines
文件大小：193KB
文件格式：PDF
更新时间：2016-01-17 04:43:08
机器学习 estricted Boltzmann machines (RBMs) have been used as generative models of many different types of data including labeled or unlabeled images (Hinton et al., 2006a), windows of mel-cepstral coefficients that represent speech (Mohamed et al., 2009), bags of words that represent documents (Salakhutdinov and Hinton, 2009), and user ratings of movies (Salakhutdinov et al., 2007). In their conditional form they can be used to model high-dimensional temporal sequences such as video or motion capture data (Taylor et al., 2006) or speech (Mohamed and Hinton, 2010). Their most important use is as learning modules that are composed to form deep belief nets (Hinton et al., 2006a).