RBNN application and simulation in big data set classification.

Text classification technology, an important basis for text mining and information retrieval, is mainly to determine the text category according to the text content under a predetermined set of categories. Traditional manual text categorization has gradually failed to meet the needs, while automatic text categorization based on artificial intelligence has become an important research direction in the field of natural language processing. To this end, this paper introduced the RBNN-based classification algorithm by considering the high dimensionality, non-linearity and complex correlation between feature items, and the theoretical and feasibility analysis were carried out so as to apply it to text feature dimension reduction. Also, the effects of the distribution density of the radial basis function in the radial basis neural network and the normalized form of the input data on the classification results were studied. Through the computer simulation experiment, the influence rule of distribution density of the radial basis function in the radial basis neural network and the normalized form of the input data on the training precision and test accuracy of the classification process were demonstrated in the form of curves, which provides guidance for the application of RBNN in pattern recognition. [ABSTRACT FROM AUTHOR]