本文研究文本的自动分类算法。
This paper mainly focus on the text classification algorithms.
邻近分类算法是个典型的分类算法。
分类规则的精度取决于分类算法的构造。
The precision of classification rule is decided by the construction of classification algorithm.
本文研究基于SLIQ的数据挖掘分类算法。
This paper studies data mining classification calculation of SLIQ.
本文提出一种基于区域分割的随机树分类算法。
We present a novel segmentation randomized tree based classification algorithm in this paper.
在特征分类算法中，本文选择简单的聚类算法。
And we choose simple Cluster algorithm for the classification.
其中消极学习型中应用最广泛的是最近邻分类算法。
In those lazy learning algorithms most extensively used is nearest neighbor classification (NN) algorithm.
介绍了流分类算法的概念以及对流分类算法的要求；
The conception of packet classification and request for it is introduced.
最后，我们提出了一种基于抽样的快速数据分类算法。
Finally, we present a fast data classification algorithm based on sampling.
从数据挖掘的观点来看，它们都与分类算法密切相关。
Those are tightly associated with classification algorithm on the view of data mining.
本文提出了一种基于决策树分类器的数据包分类算法。
The thesis researches an algorithm based on decision tree classifier for packet filtering.
给出了一种基于编码二叉树的支持向量的多类分类算法。
Produce an algorithm based on encoding binary tree and supporting vector multi-category classification algorithm.
这是以往的模板匹配算法和特征分类算法所不能达到的。
The precision is higher than those of the existing template matching algorithms or characteristic extraction algorithms.
包分类算法的性能对网络的时延和吞吐量有决定性的影响。
Performance of packet classification algorithm is an important factor of delay and throughput of network.
对KNN文本分类算法的理论研究和实际应用起了指导作用。
It plays an instructional role in academic study and practical application of KNN text classification algorithm.
引入标题权重系数改进词语权重，并提出了一种新的分类算法。
The algorithm imports title weight coefficient to improve the term weighting in conjuction with a new classification algorithm.
在数据挖掘中存在多种算法，决策树分类算法是应用比较多的一种。
There are some various algorithms in data mining, and decision tree classification algorithm is the most popular one.
然后本文探讨了声音分析和分类算法，同时给出了动态视频摘要生成算法。
It is followed by the video abstracting algorithm that takes advantage of audio content analysis and classification.
集成学习是当前机器学习的一个研究热点，它可以提高分类算法的泛化性能。
Ensemble learning is a research hotspot in machine learning, which can improve generalization performance of classification algorithm.
现有的数据分类算法大体可以划分为两大类：积极学习方法与消极学习方法。
Existing data classification algorithms may be mainly divided into two kinds: Active learning methods and lazy learning methods.
提取网络入侵模式所用的主要有分类算法、关联规则算法和序列规则算法等。
Major methods to distill network intrusion mode are class algorithm, association rule algorithm and frequent episodes algorithm.
依据这两个准则，本文提出了一种改进的基于基因功能树的基因功能分类算法。
According to these two algorithms, this thesis proposed a new gene function classification algorithm based on gene function tree.
通过对决策树分类算法的比较，本文采用C4.5决策树算法实现自学习模块。
Comparing with Decision Tree algorithms, this system chooses the C4.5 to realize the self-learning module.
针对中文网页分类问题该文设计了一种新的基于代表样本动态生成的分类算法。
A new algorithm based on representative samples dynamical generation for Chinese Web page classification was proposed in this paper.
提出平行坐标数据可视化技术与分类算法集成到一起进行可视化数据分类的方法。
A method of visual data classifying is improved by integrating visual technology of parallel coordinates with data classification algorithms.
讨论了离散贝叶斯分类算法之后，推导了离散贝叶斯分类器的分类误差估算公式。
The algorithm of discrete Bayes classifier is proposed. Then, formulas for estimating classifying error of Bayes classifier are deduced.
实验结果表明，该分类算法对于分类地物目标，进而分析其散射机理是十分有效的。
Experimental results indicate that this classification algorithm is very efficient in classifying targets and analyzing the scattering mechanism.
为了验证分析的正确性，本文尝试了几种类型的分类算法对银行全成本数据进行训练。
To verify the correctness of our method, this paper compares the performances of different classification algorithms on the data of bank cost analysis.
通过引入并行流分类算法说明了流分类算法的研究重点是减小存储空间和提高更新速度。
The presentation of parallel packet classification indicates how to reduce the storage space and update time is the research importance.
通过引入并行流分类算法说明了流分类算法的研究重点是减小存储空间和提高更新速度。
The presentation of parallel packet classification indicates how to reduce the storage space and update time is the research importance.
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