Orre Roland, Bate Andrew, Norén G Niklas, Swahn Erik, Arnborg Stefan, Edwards I Ralph
Mathematical Statistics, Stockholm University, SE-106 91 Stockholm, and NeuroLogic Sweden AB, Solna, Sweden.
Int J Neural Syst. 2005 Jun;15(3):207-22. doi: 10.1142/S0129065705000219.
A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.
本文描述了一种经过改进以处理高度不完整训练数据的递归神经网络。在世卫组织药物不良反应数据库中展示了无监督模式识别。将其与一种成熟的方法AutoClass进行了比较，并在模拟数据上研究了这两种方法的性能。在模拟数据中，神经网络方法的表现与AutoClass相当，而在实际数据中则优于AutoClass。由于其更好的扩展性，神经网络是在不完整观测的大型数据库中进行无监督模式识别的一种有前途的工具。
