bayes造句
1. A method based on the bayes and machine readable dictionary was proposed, which could disambiguate by the training of a small-scale corpus and the definition of semantic in machine dictionary.
2. Ontology ; Na ? ve Bayes Classifier; Formal Concept Analysis; Document Classification; Ten - fold Cross Validation.
3. To thoroughly understand the Bayesian network methodology, we start with the basic ideas of Bayes' Theorem and empirical Bayes method along with some illustrating examples.
4. The experiment of Naive Bayes classification indicates that this method can effectively improve classification precision of Chinese texts.
5. Combination of subjective bayes method, Certainty factor theory and fuzzy comprehensive evaluation method are applied to solve the uncertainty, dynamics and fuzzy in student evaluation.
6. Bayes was one of two main influences on the early development of probability theory and statistics.
7. On the basis of analyzing a variant of Bayes theorem and the evaluation of condition attribute with correlation, SANBC is proposed.
8. The thesis discuss the application of Bayes Theorem and its generalization on construction project.
9. As an, an example of the proposed empirical Bayes model introduced.
10. We embed Bayes learning mechanism on the basis of the negotiation model, and elaborate process descriptions of evaluating offers, belief revision and proposing counter-offers are presented.
11. But use Bayes analysis to deal with the research of Poisson process are still not complete.
12. Naive - Bayes model can integrate the prior information and the sample information.
13. Naive Bayes classifier is a simple and effective classification method. Classifying based on Bayes Technology has got more and more attentions in the field of data mining.
14. According to Bayesian theory and Bayes Factor, the posterior probability of the calculation sample belonging to a model is calculated.
15. These variables fishers and Bayes discriminant functions to distinguish sexual dimorphism the three strains.
16. Bayes classifier model is a powerful tool for classifying attack types in intrusion detection.
17. Naive Bayes Classification is a sort of statistics classification. This paper introduces method of NBC and pattern of computer-aided diagnosing for uterine myoma.
18. The Society wish to extend their sincere thanks to Mr Bayes for information and photos.
19. The accuracy of the experiment is 91.89% in open test and 99.4% in close test, substantiating the wonderful performance of dependency relationship analysis and Bayes Model.
20. It was the highlights of the paper that the method combined the explicit features and naive bayes classifier together to identify both of the encrypted and not encrypted P2P traffic.
21. When the variance was known and the conjugate prior distribution was normal, a Bayes estimation of loss-function and risk function of logarithmic normal distribution was given.
22. At last, a data-mining based model of HIDS is presented, the weighted association rules and Bayes classification algorithm is discussed too.
23. Pointing to the classification problem of Web pages, this paper proposes a classification algorithm combining rough set and Bayes classifier.
24. We consider a couple of unimodal priors on the change-point first and use ML-II approach to obtain the empirical Bayes estimators in this paper.
24.try its best to gather and build good sentences.
25. In this paper, the observed data in the Jiaozhou Bay are used to explore the application of Bayes classification method to the research field.
26. This paper uses the improved K-means (IKM) algorithm to process the missing data and thus improve the precision of the Naive Bayes classifier.
27. In Expert system, usually probability is defined as subjective credit degree of experts to evidence and regulation, and Bayes theorem is key solution in probability reasoning.
28. In order to find weighted vector for decision with multiple objectives, the paper gives method of random processing and result of Bayes estimation.
29. However, for this article, I'll show only the Naive Bayes approach, because it demonstrates the overall problem and inputs in Mahout.
30. While this sounds perhaps like a little too much freedom, this view comes with a rule for updating probability in light of new observations, known as Bayes theorem.
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