簡易貝氏分類器因為它本身運算速度的優勢，已經相當的廣泛應用在文件分類的領域中，其中因為文件本身字的分配不同，故簡易貝氏分類器在應用上有學者提出一些不同的模型，如二元獨立模型、多項式模型、卜瓦松模型，負二項式模型…等等。但有學者研究提出，簡易貝氏分類器使用多項式模型比起其他模型還是擁有較好的分類正確率，故本研究將針對多項式模型來進行其先驗分配的參數設定研究。簡易貝氏分類器做文件分類時，使用狄氏分配、以及廣義狄氏分配為簡易貝氏分類器的先驗分配，在使用廣義狄氏分配時，較令人困擾的地方為設定那數以千計的參數，本研究先將每個相異字在各類別中，依出現頻率由高至低排序，再利用分組的模式來進行參數設定的方法，期望以達到降低設定參數的複雜度，提高分類正確率以及運算效率為目標。本研究使用MDR88資料檔進行實證，期望藉由本研究之參數設定方法能擁有較高之分類正確率，同時整理出MDR88資料檔適用於哪個先驗分配將擁有最高之分類正確率，以及探討參數的調整方法以及參數值的變化對分類正確率的影響。
根據實驗結果，當使用調整先驗分配參數的方法無法顧及各類別文件的重要性時，將無法帶來改善正確率的好處；相反的參數各類別個別調整的方法可以顧及各類別文件的重要性，實驗證明，此調整參數值的方法確實可以改善正確率。
另外各個字對於類別的影響程度是不同的，所以應該考慮各個字對於類別的影響性，故本研究導入廣義狄氏分配，藉由本論文提出分組的概念，以組為單位調整參數，除了提高了分類器調整參數的效率，也顧及各組字對於影響類別重要性的不同，並經由實證，此調整參數值的方法可以再度改善分類正確率。

The naïve Bayes classifier is a popular technique for text classification because it performs well and has low computation complexity. Due to the various type of the distribution of the words in documents, there are some probabilistic model has been proposed such as binary independence model, multinomial model, poisson model, the negative model…etc. Previous studies have found that the multinomial model usually gives higher classification accuracy than the binary independence model. In this study, we use the multinomial naïve Bayes classifier for text classification and focus on the impact of setting the parameters of prior distributions.
In the multinomial naïve Bayes model, we assume the prior distribution to be either a Dirichlet or a generalized Dirichlet distribution. Setting the large amount of parameters becomes an issue when we use generalized Dirichlet distributions as priors. In order to reduce the computation complexity and obtain higher accuracy, we separate the parameters into several groups and propose five methods to systematically change the parameters corresponding to a group. We use data set MDR88 in our analysis.
By the experiment result, the concurrent prior setting method cannot get a better classification accuracy because it ignores the influence of the document in each class. On the contrary, if we consider the influence of the document in each class, it also means we should use the individual prior setting method that does improve the accuracy.
Since every word may play an important role in certain class, it is improper to adjust all parameters in a prior concurrently. We try to release this restriction by using generalized Dirichlet distributions as priors and the concept of separating parameters in groups. The experiment result shows that individual prior setting in group can get a higher classification accuracy.

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