基于上述分析，提出了决策树优化算法。
Based on the above analysis, a new algorithm of decision tree induction is proposed.
在这个过程中，应用了决策树归纳学习的优化原则，使得生成的决策树能最简洁、准确地描述神经网络学到的知识。
In the process of constructing tree, three optimization principles are adopted to concisely and accurately describe the knowledge that the networks have learned.
其次，在解决工艺参数优化的问题中，本文提出了一种正演的方法，即结合使用决策树分类器以及人工神经网络进行综合分析的方法来完成。
Secondly, in solving the problem that the craft parameter is optimized, this paper has put forward a method to perform, which using decision tree and ANN carry on comprehensive.
提出了一种适合于大规模高维数据库的组合优化决策树算法。
A combined optimization decision tree algorithm suitable for a large scale and high dimension data-base is presented.
并描述了从组织协作网到决策树的生成过程，对生成过程的求解采用了优化协作树算法。
The process of generating decision making tree from collaboration net is presented and the algorithms to optimize collaboration tree are adopted to construct the best decision making tree.
虽然其在规则设置与参数优化上还是存在着一些不足，但仍可以看出计算动词决策树是一个非常强大和有效的工具。
Though there are some shortages in rules setting and parameter optimization, the computational verb decision trees are powerful and useful tools.
分析现有信誉模型，提出一种使用信任机制和推荐机制的P 2 P信誉模型，利用决策树思想优化该模型。
This paper analyzes existing reputation models, proposes a P2P reputation model using trust mechanisms and recommendation mechanisms, and optimizes the model by the mentality of decision tree.
分析现有信誉模型，提出一种使用信任机制和推荐机制的P 2 P信誉模型，利用决策树思想优化该模型。
This paper analyzes existing reputation models, proposes a P2P reputation model using trust mechanisms and recommendation mechanisms, and optimizes the model by the mentality of decision tree.
应用推荐