| 1. | 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 |
| 2. | 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。 |
| 3. | 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 |
| 4. | 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。 |
| 5. | 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。 |
| 6. | 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。 |
| 7. | 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。 |
| 8. | 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。 |
| 9. | 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。 |
| 10. | 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。 |
