朴素贝叶斯算法，可使用对象进行分类，通常是二进制类。
Naive Bayes is an algorithm that can be used to classify objects into usually binary categories.
文章以朴素贝叶斯算法为例，详细描述了性能预测模块的构建过程。
This paper takes Naive Bayes Classifier as an illustration to describe how to construct a prediction module in detail.
实验结果表明，与传统的朴素贝叶斯算法相比，该方法具有更好的性能。
The experimental results show that this algorithm has better performance when compared with traditional naive Bayesian algorithm.
一个简单的机器学习算法，朴素贝叶斯算法可以把正规邮件从垃圾邮件里面分离出来。
A simple machine learning algorithm called naive Bayes can separate legitimate email from spam email.
在垃圾邮件分类和朴素贝叶斯算法研究的基础上，提出了基于用户知识的贝叶斯分类算法。
An user knowledge based na? Ve bayes classifier was proposed in order to conquer the problem that most of the E-mail is unstructured and need users decoding.
朴素贝叶斯算法是一种简单而高效的分类算法，但是它的条件独立性假设影响了其分类性能。
Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.
基于内容的过滤算法大多数是基于向量空间模型的算法，其中广泛使用的是朴素贝叶斯算法和K最近邻（KNN）算法。
Most of the content-based filtering algorithms are based on vector space model, of which Naive Bayes algorithm and K-Nearest Neighbor (KNN) algorithm are widely used.
本文利用改进的K -均值算法对缺失数据进行处理，提高了朴素贝叶斯分类的精确度。
This paper USES the improved K-means (IKM) algorithm to process the missing data and thus improve the precision of the Naive Bayes classifier.
文中针对该算法这两个最主要的缺陷，提出增量学习概念，引入损失幅度参数，改进和完善朴素贝叶斯分类算法。
Then in allusion to these two important factors, a concept of incremental learning and a loss extent parameter are put forward in this paper, and Native Bayesian Classification.
将聚类算法引入到朴素贝叶斯分类研究中，提出一种基于聚类的朴素贝叶斯分类算法(CNBC)。
A Naive Bayesian classification based on clustering principle (CNBC) by introducing clustering algorithm into Naive Bayesian classification.
对朴素贝叶斯分类算法进行拓展，使其应用到多关系数据分类领域，并引入了用户指导的概念。
We extended the Naive Bayesian Classifier, applied it in the relational classification filed, and introduced the concept of user's guidance.
地物类别的稳定性是BP识别算法效率的瓶颈，导致其健壮性不如朴素贝叶斯网络。
The stability of object types is one bottleneck in BP, leading to its robustness less than naive Bayesian network.
地物类别的稳定性是BP识别算法效率的瓶颈，导致其健壮性不如朴素贝叶斯网络。
The stability of object types is one bottleneck in BP, leading to its robustness less than naive Bayesian network.
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