There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
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。
In order to implement the parameter estimate in cable fault location system, an improved Bayes algorithm used for Model parameter estimate is proposed in this paper.
为实现电缆故障定位系统的参数估计，提出了一种用于模型参数估计的改进贝叶斯算法。
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.
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。
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。
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.
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。
A simple machine learning algorithm called naive Bayes can separate legitimate email from spam email.
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。
The algorithm of discrete Bayes classifier is proposed. Then, formulas for estimating classifying error of Bayes classifier are deduced.
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。
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.
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。
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。
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。
The paper used the Bayes regularization algorithm to train the BP network, the precision and generalization of which are better than the network that uses ordinary training algorithms.
本文采用贝叶斯规则化的训练方法，训练好的BP网络较常用的训练方法具有更好的精度和泛化能力。
To realize video object segment, we proposed one algorithm base on Bayes decision-making theory with least risk and video sequence edge information.
为了实现较完整的视频对象分割，提出了一种基于视频图像边缘信息和最小错误率的贝叶斯决策理论的视频对象分割算法。
After speed of statistical convergence and MSE (Mean Square Error) of these algorithms are compared, Bayes Generalization algorithm is assessed to be the most appropriate.
在比较收敛速度和均方差后，确定选用贝叶斯正则算法建立模型。
After speed of statistical convergence and MSE (Mean Square Error) of these algorithms are compared, Bayes Generalization algorithm is assessed to be the most appropriate.
在比较收敛速度和均方差后，确定选用贝叶斯正则算法建立模型。
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