decision tree造句
61. Decision tree is a tree - shaped diagram used to indicate the processing logic as a tool.
62. Then the decision tree and class association rules mining are used on the video attribute database to extract a decision tree classification rule set and a class association rule set respectively.
63. On the basis of analysing the multivalue bios, this paper proposes a new decision tree algorithm, AF algorithm, which avoids the multivalue bios problem.
64. This paper uses a new attribute selection metric to construct decision tree, called the gain-ratio criterion, replace the gain criterion.
65. It improves the accuracy of attribute selection, overcomes the impact of noise data effectively and strengthened generalization ability of the decision tree.
66. Introduces decision tree and points out its key techniques: the choice of testing feature and tree pruning.
67. Decision tree learning is one of the widely used and practical methods for inductive inference.
68. A hierarchical decomposed support vector machines binary decision tree is used for classification.
69. Multiway decision tree has important applications in pattern recognition, artificial intelligence and decision support systems.
70. The proposed pruning stragey reduced computational complexity of the decision tree.
71. In the course of researching, we accomplish a Decision Tree classifier.
72. Control decision was made time by searching the decision tree for the path of minimum cost.
73. The paper briefly introduces the concept of privacy preserving data mining technology and studies the application of decision tree classifier in this particular field.
74. Experiments result proves that decision tree classifier is a effective classify method.
75. When analyze financial factor, decision tree method on the basis of inductive reasoning means is adopted to analyze the infection of financial factor to loan risk classification.
76. In bidding stage qualitative and quantitative analysis shall be applied for risk analysis, including methods of expert scoring, decision tree, expected loss, hierarchy analysis, and fuzzy evaluation.
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