关键词: 数值属性, 分类, 深度置信网络, 联想记忆

Abstract: Deep Belief Network（DBN） is a deep architecture that consists of several Restricted Boltzmann Machines（RBM）. Generally the inputs of RBM are binary vector which leads to the information loss and in turn degrades the performance of classification. For the problem above, a DBN classification algorithm for numerical attribute is proposed through scaling the input into the interval between 0 and 1 with adding noise to sigmoid units, and achieving classification with one Gaussian hidden node on the top-level RBM. DBN can be used as feature extraction method as well as neural network with initially learned weights. DBN should have a better performance than the traditional neural network due to the initialization of the connecting weights rather than just using random weights in neural network. Experiments conducted on the dataset from UCI show that the proposed algorithm has a better accuracy than the traditional classification algorithm like SVM.

Key words: numerical attribute, classification, Deep Belief Network（DBN）, associate memory