复合句结构学习；
数据结构学习资料，请大家好好打好基础，谢谢支持，ch3 ！
Data structure learning materials, please take a good foundation, thanks to support, ch3!
针对数据的复杂性和语义深层关系，提出一种李群深层结构学习算法。
For the complexity of data and the deep relationship of semantic, a Lie Group deep structure learning algorithm is proposed.
结构学习确定了表示了模糊规则和模糊分段数的连接类型以及隐节点数目。
The structure learning decides the proper connection types and the number of hidden units which represent fuzzy logic rules and the number of fuzzy partitions.
研究了贝叶斯网络的学习问题，包括贝叶斯网络结构学习和贝叶斯网络参数学习。
The learning of Bayesian Networks is studied, including structure learning of Bayesian Networks and parameter learning of Bayesian Networks.
详细分析了贝叶斯网络的建模过程，即贝叶斯网络的结构学习过程和贝叶斯网络的参数学习过程。
In the process of modeling BNs, the structure learning and parameter learning of BNs are analyzed detailedly.
文中提出了一种新的结构学习TANC - CBIC算法。并在贝叶斯分类器实验平台MBNC上编程实现。
This paper suggests a new structure-learning algorithm called TANC-CBIC, makes experiment in MBNC experiment platform with programming TANC-CBIC algorithm.
用TANC—BIC结构学习算法构建的分类器取得了成功，但TANC—BIC结构学习算法未考虑类节点的情况。
The classifier which was set up by the TANC - BIC structure - learning algorithm bad acquired success, but it didn't consider the class node.
使用K2结构学习算法选出具有类别可分性的波段，进一步利用互信息测试对遥感波段之间的相关性做分析，去除冗余信息。
Firstly to select the bands that have class separability by K2 algorithm, then remove the redundant bands based on conditional mutual information test.
本文在分析了多种贝叶斯网络结构学习算法的基础上，并且根据水电仿真的应用背景，提出了一种根据多专家提供的规则库进行贝叶斯网络结构学习的新算法。
The Thesis analyses many kinds of Algorithm about Bayesian network structure learning, and then Setting-up a new Algorithm about structure learning Foundation on hydro-electrical simulation system.
本文在分析了多种贝叶斯网络结构学习算法的基础上，并且根据水电仿真的应用背景，提出了一种根据多专家提供的规则库进行贝叶斯网络结构学习的新算法。
The Thesis analyses many kinds of Algorithm about Bayesian network structure learning, and then Setting-up a new Algorithm about structure learning Foundation on hydro-electrical simulation system.
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