bayesian classification造句
例句与造句
- Bayesian classification model based on attribute correlation analysis
基于属性相关性分析的贝叶斯分类模型 - According to the criteria , the advancement of bayesian classification is evident
综合这几个指标，贝叶斯分类算法的优点较为突出。 - The bayesian classification and identification method based on normal - inverted wishart prior distribution
先验分布的贝叶斯分类识别方法研究 - The often - used classification is classification by decision tree induction , bayesian classification and bayesian belief networks , k - nearest neighbor classifiers , rough set theory and fuzzy set approaches
分类算法常见的有判定树归纳分类、贝叶斯分类和贝叶斯网络、 k -最临近分类、粗糙集方法以及模糊集方法。 - There are many techniques for data classification such as decision tree induction , bayesian classification and bayesian belief networks , association - based classification , genetic algorithms , rough sets , and k - nearest neighbor classifiers
挖掘分类模式的方法有多种，如决策树方法、贝叶斯网络、遗传算法、基于关联的分类方法、粗糙集和k -最临近方法等等。 - It's difficult to find bayesian classification in a sentence. 用bayesian classification造句挺难的
- Naive bayesian classification algorithm is not satisfying when deployed to continuous attribute . therefore , the paper proposes a new discretization method under the hint of holte ' s 1r ( one rule ) discretization technique and the mechanism of entropy
朴素贝叶斯分类算法应用于连续属性值时并不太理想，为此本文结合holte的1r离散化方法和熵的原理，提出了一种新的离散化方法。 - Bayesian classification is based on bayesian theorem . it can be comparable in interpretability with decision tree and in speed with neural network classifiers . bayesian classifiers have also exhibited high accuracy and speed when applied to large databases
该算法基于贝叶斯定理，可解释性方面可以与判定树相比，准确度可和神经网络分类算法相媲美，用于大型数据库时该算法已表现出高准确度与高速度。 - Unlike other classifications , bayesian classification bases on mathematics and statistics , and its foundation is bayesian theory , which answers the posterior probability . theoretically speaking , it would be the best solution when its limitation is satisfied
与其它分类方法不同，贝叶斯分类建立在坚实的数理统计知识基础之上，基于求解后验概率的贝叶斯定理，理论上讲它在满足其限定条件下是最优的。 - After dividing proper nouns in two categories , this paper discusses different algorithms for these two categories : for the first category we use proper nouns database to recognize it , and for the second category we use the recognizing method base on native bayesian classification algorithm
然后对这两类专有名词设计不同的识别方法:对第一类专有名词使用的基于专有名词词库的识别算法;对第二类专有名词使用的基于朴素贝叶斯分类的识别算法。 - Monte carlo is a method that approximately solves mathematic or physical problems by statistical sampling theory . when comes to bayesian classification , it firstly gets the conditional probability distribution of the unlabelled classes based on the known prior probability . then , it uses some kind of sampler to get the stochastic data that satisfy the distribution as noted just before one by one
蒙特卡罗是一种采用统计抽样理论近似求解数学或物理问题的方法，它在用于解决贝叶斯分类时，首先根据已知的先验概率获得各个类标号未知类的条件概率分布，然后利用某种抽样器，分别得到满足这些条件分布的随机数据，最后统计这些随机数据，就可以得到各个类标号未知类的后验概率分布。 - In tcm this pattern is called pair of medicine , and it can be resolved by frequent pattern mining . the symptom complex diagnose can be treated as a bayesian training and a bayesian classification on large clinical database cases . the critical step to resolve the chinese prescription compounding is to build an appropriate model to express the progress of it
中药知识发现集中在发现常用的单味药合用模式，在中医术语中称之为药对，这可以用高频集发现来解决；中医症候诊断可以看成是在大量临床案例库上的贝叶斯训练器和分类器；解决方剂配伍问题的关键是建立起一个合适的配伍计算机模型。 - This paper mainly deals with the multivariate bayesian inference theory used in the modern economical and management science . this includes the bayesian inference theory about three important kinds of linear models , including the single equation model , multiple equation model system and var ( p ) predictive model , and their application in economic forecasting and quality control , and also the design for the bayesian classification identification method among multiple populations
本文主要研究现代经济管理中的多元贝叶斯推断理论，包括单方程模型、多方程模型系统和向量自回归var ( p )模型的贝叶斯推断理论及其在经济预测与质量控制中的应用，以及多总体的贝叶斯分类识别方法的构造。 - Pvm , belongs to now , has been used widely . the paper implements the parallel algorithm of optimization bayesian classification on pvm , and analyzed acceleration rate and complexity . the analysis indicates that it is excellence when where is amount of class or the data is very large
本论文在pvm的基础上研究并实现了优化贝叶斯算法的并行化，并且分析了该算法的加速比和时间复杂度，分析表明在类比较多、或者待分类的数据样本比较多时，用该并行算法可以较大幅度提高数据分类的效率。 - Nowadays , many classification methods and some prediction technologies have been put forward , such as classification by decision tree induction , bayesian classification , classification by backpropagation , k - nearest neighbor classifiers , linear and nonlinear regression . however , none of them is better than others in all application
在这一研究方向，目前已提出了多种分类方法（如决策树归纳分类、贝叶斯分类、神经网络分类和k -最邻近分类等）和一些预测技术（如线性回归、非线性回归等） 。