讨论了离散贝叶斯分类算法之后，推导了离散贝叶斯分类器的分类误差估算公式。
The algorithm of discrete Bayes classifier is proposed. Then, formulas for estimating classifying error of Bayes classifier are deduced.
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.
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.
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.
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.
现有的分类预测的方法有许多种，常见的有决策树算法(C4.5)、贝叶斯分类算法、BP算法与支持向量机等。
There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
为了解决该方法存在的训练数据集问题，本文改进了现有的贝叶斯分类算法，提出了利用未标记数据提高贝叶斯分类器性能的方法。
In order to solve the problem existing in training data sets, present Bayes algorithm is im - proved and an algorithm using unlabeled data to improve the capability of the classifier is proposed.
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.
并且与图像分类中统计方法的经典算法贝叶斯分类方法做了比较，结果发现，神经网络分类方法的分类效果要优于贝叶斯方法。
Comparing with Bayes method-the classical algorithm, we conclude that the neural network is better than Bayes method. This paper gives all the procedures of SAR image classification.
目前遥感影像分类的常用模型和算法有统计学方法、神经网络、贝叶斯等。
Current remote sensing image classification models and algorithms commonly used statistical methods, neural networks, Bayesian and so on.
贝叶斯方法是那些明确地在分类和回归问题中应用贝叶斯定理的算法。
Bayesian methods are those that are explicitly apply Bayes' Theorem for problems such as classification and regression.
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.
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.
方法收集343种传染病症状、体征数据，运用贝叶斯算法建立分类模型。
Methods The data on 343 kinds of infectious disease symptoms, signs were collected and Bayesian classification model was constructed.
方法收集343种传染病症状、体征数据，运用贝叶斯算法建立分类模型。
Methods The data on 343 kinds of infectious disease symptoms, signs were collected and Bayesian classification model was constructed.
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