Varying Naïve Bayes Models With Applications to Classification of Chinese Text Documents
时变朴素贝叶斯模型及其在中文文本分类中的应用
Document classification is an area of great importance for which many classification methods have been developed. However, most of these methods cannot generate time-dependent classification rules. Thus, they are not the best choices for problems with time-varying structures. To address this problem, we propose a varying naïve Bayes model, which is a natural extension of the naïve Bayes model that allows for time-dependent classification rule. The method of kernel smoothing…