naive bayesian classification

基本解釋

We extended the Naive Bayesian Classifier, applied it in the relational classification filed, and introduced the concept of user's guidance.
對(duì)樸素貝葉斯分類算法進(jìn)行拓展，使其應(yīng)用到多關(guān)系數(shù)據(jù)分類領(lǐng)域，并引入了用戶指導(dǎo)的概念。
For example, NLTK has a whole framework for text classification using statistical techniques like "naive Bayesian" and "maximum entropy" models.
例如，NLTK 有一個(gè)完整的框架，用于通過(guò)類似于“naive Bayesian” 和“maximum entropy”等模型的統(tǒng)計(jì)技術(shù)進(jìn)行文本分類。
The Bayesian classification technology in Data mining was discussed, and the research was emphasized on the basic principle and the work procedure of the Naive Bayesian classification technology.
對(duì)數(shù)據(jù)挖掘中的貝葉斯分類技術(shù)進(jìn)行了討論，重點(diǎn)分析了樸素貝葉斯分類技術(shù)的基本原理和工作過(guò)程。

樸素貝葉斯分類
Some basic theories used in this thesis are briefly introduced, including Naive Bayesian classification modeling, the basic problems in Machine Learning, Support Vector Machine classification algorithms and the character of Copula function.
本文簡(jiǎn)要地介紹了關(guān)于樸素貝葉斯分類器和支持向量機(jī)的基本理論,包括樸素貝葉斯分類模型,機(jī)器學(xué)習(xí)的基本問題,統(tǒng)計(jì)學(xué)習(xí)理論的基本內(nèi)容,支持向量機(jī)分類算法,Copula函數(shù)的原理及性質(zhì)。