片语

Naive Bayes Classification朴素贝叶斯分类

Discriminative Naive Bayes区分式朴素贝叶斯

General Naive Bayes广义朴素贝叶斯

Gaussian Naive Bayes高斯朴素贝叶斯

Complementary Naive Bayes classifier补充朴素贝叶斯分类器

naive bayes text classifier贝叶斯文本分类

英汉例句

• If the NB conditional independence assumption actually holds, a Naive Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data.

  倘若条件独立性假设确实满足，朴素贝叶斯分类器将会比判别模型，譬如逻辑回归收敛得更快，因此你只需要更少的训练数据。

• Naive Bayes classifiers often break down when the size of the training examples per class are not balanced or when the data is not independent enough.

  当各类的训练示例的大小不平衡，或者数据的独立性不符合要求时，Naive Bayes分类器会出现故障。

• 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.

  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。

• Many algorithms are used to create supervised learners, the most common being neural networks, Support Vector Machines (SVMs), and Naive Bayes classifiers.

  创建监管学习程序需要使用许多算法，最常见的包括神经网络、SupportVectorMachines (SVMs)和Naive Bayes分类程序。

• Naive Bayes classifier is a simple and effective classification method. Classifying based on Bayes Technology has got more and more attentions in the field of data mining.

  朴素贝叶斯分类器是一种简单而高效的分类器，基于朴素贝叶斯技术的分类是当前数据挖掘领域的一个研究热点。

• Naive Bayes is an algorithm that can be used to classify objects into usually binary categories.

  朴素贝叶斯算法，可使用对象进行分类，通常是二进制类。

• This paper takes Naive Bayes Classifier as an illustration to describe how to construct a prediction module in detail.

  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。

• Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes in the real world.

  朴素贝叶斯分类器是一种简单而高效的分类器，但是其属性独立性假设限制了对实际数据的应用。

• Naive Bayes classifiers are known to be fast and fairly accurate, despite their very simple (and often incorrect) assumptions about the data being completely independent.

  Naive Bayes分类器为速度快和準确性高而着称，但其关于数据的简单(通常也是不正确的)假设是完全独立的。

• Naive Bayes classification is a kind of simple and effective classification model. However, the performance of this model may be poor due to the assumption on the condition independence.

  朴素贝叶斯分类是一种简单而高效的分类模型，然而条件独立性假设在现实中很少出现，致使其性能有所下降。

• The first approach is a simple Map-Reduce-enabled Naive Bayes classifier.

  第一种方法是使用简单的支持Map - Reduce的Naive Bayes分类器。

• Naive Bayes classifier is a simple and effective classification method based on probability theory, but its attribute independence assumption is often violated in the real world.

  朴素贝叶斯分类器是一种简单而有效的概率分类方法，然而其属性独立性假设在现实世界中多数不能成立。

• The experiment of Naive Bayes classification indicates that this method can effectively improve classification precision of Chinese texts.

  基于朴素贝叶斯分类方法的实验表明，提出的方法能够有效提高中文文本的分类準确率。

• Although the Naive Bayes spam filter is simple and convenient, the recall and precision are hard to be improved.

  虽然朴素贝叶斯邮件过滤器计算简便，但召回率和正确率都难以进一步提高。

• Naive Bayes algorithm is a simple and effective classification algorithm. However, its classification performance is affected by its conditional attribute independence assumption.

  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 is easy to implement and fast, so it is widely used.

  其中朴素贝叶斯具有容易实现，运行速度快的特点，被广泛使用。

• TAN classifier extends the structure of Naive Bayes classifier by adding augmenting arcs that obey certain structural restrictions.

  TAN分类器按照一定的结构限制，通过添加扩展弧的方式扩展朴素贝叶斯分类器的结构。

• On the other hand, Naive Bayes is weighted by computing the confidence of association rules.

  另一方面，通过关联规则的置信度，给朴素贝叶斯加权。

• So a new Bayesian model mixed tree augmented Naive Bayes classifier(MTANC) based on the rough set theory is presented.

  因此，提出了一种基于粗糙集理论的混合树增广朴素贝叶斯分类模型（MTANC）。

• This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes classification approach based on data randomization and feature reconstruction.

  围绕着分类挖掘中的隐私保护问题展开研究，给出了一种基于数据处理和特征重构的朴素贝叶斯分类中的隐私保护方法。

• This paper USES the improved K-means (IKM) algorithm to process the missing data and thus improve the precision of the Naive Bayes classifier.

  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。