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.

31. 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.

32. This paper focuses on privacy preserving classification, and presents a privacy preserving Naive Bayes classification approach based on data randomization and feature reconstruction.

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

34. To improve efficiency, used naive Bayes classify method to reduce the searching space.

35. The optimal decision arithmetic are obtained by minimizing the average Bayes risk, the decision form of the local detectors and fusion center can be simplified as the likelihood ratio test.

36. TAN classifier extends the structure of Naive Bayes classifier by adding augmenting arcs that obey certain structural restrictions.

37. The paper expounds the application of Bayes discriminant analysis in safety evaluation.

38. Adopting the Fuzzy induce basing on bayes flow and relatedness flow and improving the reliability of diagnosis.

39. Then by Naive Bayes text classification method, a document unknown class can be classified.

40. According to Bayes theorem, a new method can be got in data diagnosis - grading method.

41. These variables were used to establish fishers and Bayes discriminant functions to distinguish sexual dimorphism among the three strains.

42. In order to implement the parameter estimate in cable fault location system, an improved Bayes algorithm used for Model parameter estimate is proposed in this paper.

43. Based on vector space model a Bayes text classification method is proposed.

44. The research of the real-word errors check mainly depends on some specific confusion sets and as well as Bayes theorem.

45. Presently , naive Bayes algorithm for Email filtering has been accepted widely for its simplicity and plainness.

46. Empirical Bayes estimation ( EBE ) and maximum likelihood estimation ( MLE ) of reliability index are given, respectively.

47. Bayes network is a directed acyclic graph with a series of conditional probabilities.

48. TAN(tree augmented Nave Bayes) takes the Nave Bayes classifier and adds edges to it, it is efficient extend of Nave Bayes.

49. The representations of statistic - based approach include Bayes and SVM ( support vector machine ) and so on.

50. To improve the technology of the textile CAD, we proposed a new way of textile image segmentation by the minimum error Bayes decision theory based on the semi-supervised clustering.

51. Rough set and Bayes classifier were integrated in virus detection.

52. The algorithm of discrete Bayes classifier is proposed. Then, formulas for estimating classifying error of Bayes classifier are deduced.

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

54. The first approach is a simple Map-Reduce-enabled Naive Bayes classifier.

54. is a online sentence dictionary, on which you can find excellent sentences for a large number of words.

55. In combination with the Bayes estimator for the parameter of linear exponential model under the same loss function, the nonparametric empirical Bayes estimator of the unknown parameter was obtained.

56. Meanwhile, we try to understand the decision - making behaviors under Bayes risk by simulating the related decisions.

57. The main idea of which consists of three parts:the discriminating feature analysis of the images, the statistical modeling of face and non-face classes, and the Bayes classifier for face detection.

58. However, proper conditions can prevent sericin from gelation and improve cocoon reelability due to weaker gumming force among bayes.

59. Bayes filtering has a dominant place in the area of filtering for its excellentcategorization , high precision.

60. Naive Bayes classification is a kind of simple and effective classification model. However, the performance of this model may be poor due to the assumption on the condition independence.

61. 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.

62. This paper analyzes the shortcomings of Bayes and puts forward a better method to improve it.

63. 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.

64. For implicit temporal expression recognition, witch is also called as Chinese situation analysis. The Bayes Classification is used for Chinese verb classification.

65. Based on the minimum error Bayes decision theory, the authors proposed a new way of image(segmentation).

66. Through the analysis of the composite base price and the introduction to the Bayesian decision, the Bayes theorem is led into determining the quoted price with the composite base price.

67. 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.

68. The posterior probability can also be expressed in terms of class-conditional density function and prior probability by the Bayes theorem.

69. But my favoritetomb is that of Thomas Bayes, the eighteenth-century statistician for whomBayesian filtering is named.

70. The second method uses Bayes classifier in the first step and decision tree classifier in the second step.

71. In this paper, we investigate enhancement of naive Bayes classifier using feature weighting technique.

72. The marginal posterior distribution of the parameter in the ARFIMA models is presented by Bayes theorem and the mode of the marginal posterior distribution is choosed as the estimator.

73. Aim at the document image with both Chinese Characters and English characters, this paper present a character segmentation and language discrimination method based on Simple Bayes Classifier.

74. The recognition rates for the handwritten digits and SAR image classification outperform the tradition Bayes classifier.

75. Experimental results show that Bayes classifier is suitable for the transformer fault diagnosis.

76. To tackle the over-segmentation problem, the blobs were merged iteratively with the utilization of Bayes classification rule.

77. The Bayes risk decision - making model is used to eliminate the invalidated computing packages.

78. The origin of the concept of obtaining posterior probabilities with limited information is attributable to Thomas Bayes.

79. The Bayes rule for minimum error and supervised parameter estimation for Gaussian mixture are used to solve the problem of threshold selection and get a good experimental result.

80. In designing Web Classifier, this thesis makes use of Vector Space Model to represent the web text, which improves the performance of Bayes Classifier.

81. I said my good - bayes, arranged for their temporary care and return home.

82. Conventional remote sensing image classification methods are mostly based on Bayes subjective probability theory.

83. The practice indicate that the Planting Decision-making on Cross Bedding of Farming and Animal Husbandary in the East of Inner Mongolia by the Bayes Rule is feasible.

84. There template matching classifier, Bayes classifier a linear function of classification, non - linear classification,[http:///bayes.html] neural network classifier.

85. Bayes Network is a new inference and express method of uncertain knowledge.

86. The formula of total probability , conditional probability and bayes formula are elementary formulas of probability theory.

87. In this paper, a Bayes decision rule is derived for the scale-exponential family with error in variables, and an empirical Bayes (EB) decision rule is constructed by a deconvolution kernel method.

88. Bayes classification 1) three types of covariance not equal 2) three equal covariance () 3) programming line on the machine to draw three categories (or sub-interface).

89. After analyzing several theory models of inductive reasoning, we use the Bayes Theorem to prove the premise probability principle, and integrate this theory with human mental process.

90. In this paper, a method analyseis provided, which based on AR model and Bayes taxonomy.

91. At the same time we got some preliminary estimation of the fatality rate through empirical model and the Bayes method.