Naive Bayes classification algorithm based on small sample set

Naive Bayes algorithm is one of the most effective methods in the field of text classification, but only in the large training sample set can it get a more accurate result. The requirement of a large number of samples not only brings heavy work for previous manual classification, but also puts forward a higher request for storage and computing resources during the computer post-processing. This paper mainly studies Naïve Bayes classification algorithm based on Poisson distribution model, and the experimental results show that this method keeps high classification accuracy even in small sample set.