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Description: 人工智能实验§贝叶斯网络的因果推理关系§ 熟悉掌握Bayes定理，学习贝叶斯网络的因果推理-artificial intelligence experiments Bayesian network causal reasoning relations mastery Bayes Theorem, learning Bayesian network causal reasoning
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Author: 楚随风 |
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Description: 人工智能实验§贝叶斯网络的因果推理关系§ 熟悉掌握Bayes定理，学习贝叶斯网络的因果推理-artificial intelligence experiments Bayesian network causal reasoning relations mastery Bayes Theorem, learning Bayesian network causal reasoning
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Author: 楚随风 |
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Description: Naive Bayes 分类器, 用C语言写的.大家一起参考一下. -Naive Bayes classifier, using the C language. U.S. with reference.
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Author: sj2ac |
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Description: 搜索巨人Google和Autonomy，一家出售信息恢复工具的公司，都使用了贝叶斯定理（Bayesian principles）为数据搜索提供近似的（但是技术上不确切）结果。研究人员还使用贝叶斯模型来判断症状和疾病之间的相互关系，创建个人机器人，开发能够根据数据和经验来决定行动的人工智能设备。-Search giant Google and Autonomy, a sale of information-retrieval tools which are using the Bayes theorem (Bayesian principles) for the data search to provide similar (but technically incorrect) results. The researchers also used Bayesian models to determine the symptoms and the interrelationship between diseases, create personal robots, developed based on data and experience to determine the action of artificial intelligence equipment.
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Author: longhaoqiu |
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Description: 贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我们做条件独立的假设，即给定元组的类标号，假定属性值有条件地相互独立，即在属性间不存在依赖关系。此程序仅为算法的一个实现，根据训练数据训练分类器-Bayesian algorithm is based on the Bayes theorem P (H | X) = P (X | H) P (H)/P (X).. For multi-attribute data sets, computing P (X | Ci) of the overhead is very large, in order to reduce the computational complexity, we do conditional independence assumption that a given tuple class label, it is assumed that property values conditionally independent of each other, that does not exist in the inter-attribute dependencies. This procedure is only an implementation of algorithm, according to training data classifier training
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Author: guifeng2002 |
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Description: This function calculate the performance, based on Bayes theorem, of a clinical test. The input is based on a 2x2 matrix (true positive, false positives false negative, true negatives).
The Outputs are:
- Prevalence of disease
- Test Sensibility with 95 confidence interval
- Test Specificity with 95 confidence interval
- False positive and negative proportions
- Youden s Index
- Matthews Correlation Coefficient
- Number needed to Diagnose (NDD)
- Discriminant Power
- Test Accuracy
- Mis-classification Rate
- Positive predictivity
- Negative predictivity
- Positive Likelihood Ratio
- Negative Likelihood Ratio
- Test bias
- Diagnostic odds ratio
- Error odds ratio-This function calculate the performance, based on Bayes theorem, of a clinical test. The input is based on a 2x2 matrix (true positive, false positives false negative, true negatives).
The Outputs are:
- Prevalence of disease
- Test Sensibility with 95 confidence interval
- Test Specificity with 95 confidence interval
- False positive and negative proportions
- Youden s Index
- Matthews Correlation Coefficient
- Number needed to Diagnose (NDD)
- Discriminant Power
- Test Accuracy
- Mis-classification Rate
- Positive predictivity
- Negative predictivity
- Positive Likelihood Ratio
- Negative Likelihood Ratio
- Test bias
- Diagnostic odds ratio
- Error odds ratio
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Author: Rafal |
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Description: this is a solution of bayes theorem
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Author: kinkini |
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Description: Clinical Test Performance The test calculate the performance of a clinical test based on the Bayes theorem Author: Giuseppe Cardillo-Clinical Test Performance The test calculate the performance of a clinical test based on the Bayes theorem Author: Giuseppe Cardillo
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Author: Shivakumar |
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Description: This function calculate the performance, based on Bayes theorem, of a clinical test. The input is based on a 2x2 matrix (true positive, false positives false negative, true negatives).-This function calculate the performance, based on Bayes theorem, of a clinical test. The input is based on a 2x2 matrix (true positive, false positives false negative, true negatives).
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Author: zhenhao |
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Description: Predicting the results of the 1994 Congressional election.You will be using data from the 1984, 1986, 1988, 1990 and 1992 elections along with Bayes’ theorem to determine the chance that each party (Democrats or Republicans) will win the majority of the elections for each state.
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Author: Fontana |
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Description: bayes classification using bayes theorem
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Author: RAJASHEKAR B R |
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Description: This file details bayesian classification theorem in artificial intelligence
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Author: bala |
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Description: Bayes在分类中的运用，并结合C++语言编写的程序。实现准确分类。-Application of Bayes theorem in classification, and combined with programs written in c++ language to realize accurate classification
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Author: yagwenbang |
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Description: In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes theorem with strong (naive) independence assumptions between the features.-In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes theorem with strong (naive) independence assumptions between the features.
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Author: hera |
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Description: code describing Bayes theorem for sensor data fusion
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Author: danish |
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Description: in machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes theorem with strong (naive) independence assumptions between the features.-in machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes theorem with strong (naive) independence assumptions between the features.
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Author: N Mounika |
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Description: 关于密度聚类和贝叶斯分类方法的相关代码及其应用-density clustering and Bayes Theorem for classification
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Author: 李维 |
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Description: Bayesian inference is an inference method allowing to deduce the probability of an event those of other events already uated. It relies mainly on the Bayes theorem.-Bayesian inference is an inference method allowing to deduce the probability of an event those of other events already uated. It relies mainly on the Bayes theorem.
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Author: alouey |
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Description: In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,[1]:488 and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines.[2] It also finds application in automatic medical diagnosis.[3]
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Author: sandy126
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Description: This is a simple probabilistic classifier based on the Bayes theorem, from the Wikipedia article. This project contains source files that can be included in any C# project.
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Author: daauudsalaad
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