A maximum entropy model of feature extraction for the gas disaster information is established, with the training algorithm of the maximum entropy presented.
以最大熵原理为基础，建立了最大熵瓦斯灾害信息特征提取模型，提出了最大熵模型的参数训练算法。
In addition, the data stream parsing, extraction protocol characteristics, the establishment of ATM, IP protocol type algorithm model for rapid identification.
另外，对数据流进行解析，提取协议特征，建立A TM、IP协议类型快速识别算法模型。
The selection of features depends on the algorithm of feature extraction , and we use the 14-point ASM(Active Shape Model) in this paper.
特征的选取取决于特征提取的算法，本文采用的是14点主动形状模型。
This paper proposes a new algorithm using hidden Markov model for information extraction based on multiple templates due to the variety of training data.
针对训练数据来源的多样化，提出了基于多模板隐马尔可夫模型的文本信息抽取算法。
Experimental results show that using the feature point extraction algorithm can improve the accuracy and reliability, and has better results for the model which has lots of noises.
实验结果表明，利用该算法可以提高特征点提取的准确性和可靠性，对于存在大量噪声的模型有较好的效果。
The algorithm makes use of the information of format and list separators to segment text, and then combines hidden Markov model for text information extraction.
该算法利用文本排版格式、分隔符等信息，对文本进行分块，在分块的基础上结合隐马尔可夫模型进行文本信息抽取。
A new algorithm based on hidden Markov Model is proposed for text information extraction.
提出了一种基于隐马尔可夫模型的文本信息抽取算法。
Based on a STL file of thin-wall part, which can be produced by almost each commercial CAD software, an algorithm is proposed for Mid-plane mesh model extraction.
基于商用CAD软件生成的用STL文件表示的薄壁注塑件实体模型，给出了表面有限元网格模型的生成方法。
Based on a STL file of thin-wall part, which can be produced by almost each commercial CAD software, an algorithm is proposed for Mid-plane mesh model extraction.
基于商用CAD软件生成的用STL文件表示的薄壁注塑件实体模型，给出了表面有限元网格模型的生成方法。
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