英语释义

• English mathematician for whom Bayes' theorem is named (1702-1761)

片语

Bayes principle贝叶斯原理

Bayes Filter贝叶斯滤波

bayes classification贝叶斯分类

Bayes analysis贝叶斯分析

Bayes risk贝叶斯风险

Variational Bayes变分贝叶斯

new bayes朴素贝叶斯算法

Naive Bayes朴素贝叶斯

Bayes estimatorIn estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e.

英汉例句

• We build one single price trading model of second-Hand car, and by using Bayes formula, mathematicl expectation, and backward induction ect, we get the quilibrium Solution of the model.

  本文建立单一价格二手车交易模型，并应用贝叶斯概率公式、数学期望及逆推归纳法等知识，求出了该模型均衡解。

• This paper interprets the balanced results of Bayes game with linear strategy, and gives out the definitions of electricity utility cost and electricity customers profit.

  采用线性战略组合，解出了该贝叶斯博弈的均衡解，并且定义了配电商的成本和电能消费者的收益。

• The importance of Bayes , estimation for extreme small sample, high reliability, safe or failure pattern is explained in this paper, the difficulty in solving these facts is involved.

  首先阐明了极小子样高可靠性成败型产品试验评估的重大意义及通常用贝叶斯法解决该问题时所遇到的困难。

• The Rules of Bayes have described the functions of new evidences in the knowledge growth.

  贝叶斯规则着力刻画了新证据在知识增长方面的作用，从而具有较强的知识创新功能。

• Bayes classifier model is a powerful tool for classifying attack types in intrusion detection.

  贝叶斯分类模型是入侵检测中用于攻击类型分类的有力工具。

• The misclassification probability of the nearest neighbor decision rule won「t exceed 2 times of that of Bayes decision rule when the sample number is very large.

  最近邻準则是一种次最优準则，当样本数目很大时，最近邻準则的错误率不会超过贝叶斯错误概率的2倍。

• However, for this article, I」ll show only the Naive Bayes approach, because it demonstrates the overall problem and inputs in Mahout.

  但在本文中，我只会演示NaiveBayes方法，因为这能让您看到总体问题和Mahout中的输入。

• Based on the results of Bayes estimations, the standard deviations for the compressive strength of RAC with strength grades of C20 and C30 were proposed.

  根据统计分析和贝叶斯估计结果，建议了强度等级为C20和C30再生混凝土抗压强度的标準值与标準差的取值。

• To overcome the hardship of enacting the pre-probability distribution with high certainty factor, this paper proposes one novel way of applying Bayes analysis to classify pattern.

  针对模式分类中高置信度的先验概率分布难以设定的问题，提出了一种新的应用贝叶斯分析进行模式分类的方法。

• 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NaiveBayes分类程序。

• The thesis discuss the application of Bayes Theorem and its generalization on construction project.

  本文探讨了贝叶斯定理及其推广在工程建设项目中的应用。

• 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。

• This paper discusses two methods of complete event group for complete probability formular and Bayes formular.

  对全概率公式和贝叶斯公式，探讨了寻找完备事件组的两个常用方法。

• Bayes factor is the major tool for model selection in Bayesian Statistics.

  在贝叶斯统计学中，贝叶斯因子是进行模型选择的主要工具。

• 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。

• 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。

• This paper presents an efficient automatic categorization system for Chinese journals based on Bayes classifier.

  本文设计了一个有效的基于贝叶斯分类器的中文期刊自动分类系统。

• 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NaiveBayes分类器。

• The characteristic of the Bayes method is to use probability to express the uncertainty of all forms, learning and the reasoning of other forms are all realized with the rule of probability.

  贝叶斯方法的特点是使用概率去表示所有形式的不确定性，学习或其他形式的推理都用概率规则来实现。

• 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 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，NaiveBayes分类器会出现故障。