Taking advantage of improved resource allocating network and latent semantic feature selection approach for automated text categorization
We utilize the double means clustering-based method to determine the initial centers in hidden layer. We propose the RMS sliding window approach to reduce the underlying influence of initial noise data. The former phase of RAN initializes the preliminary structure of RAN, and the latter phase refines its learning ability. The experiment results show the superiority of our method comparing with state of the art categorization algorithms.
resource allocating network semantic feature selection text categorization novelty criteria root mean square least mean square extended kalman filter neural network