This paper mainly focus on the text classification algorithms.
本文研究文本的自动分类算法。
This algorithm improves the recognition rate of pattern classification algorithms.
本算法提高了模式分类算法的辨识率。
When massive data is involved, traditional classification algorithms become inefficient.
在数据量很大时，原有的数据分类方法变得失效。
Most pattern classification algorithms rely heavily on the shape of the signal, which can vary considerably with frequency.
因为大多数模式分类算法与信号的形状密切相关，而信号的形状很大程度上随检测频率的变化而变化。
Existing data classification algorithms may be mainly divided into two kinds: Active learning methods and lazy learning methods.
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。
A method of visual data classifying is improved by integrating visual technology of parallel coordinates with data classification algorithms.
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。
To verify the correctness of our method, this paper compares the performances of different classification algorithms on the data of bank cost analysis.
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。
The algorithms of facial expression recognition system mainly contain images' preprocessing algorithms, feature extraction algorithms and classification algorithms.
人脸表情识别系统中的算法主要有图像处理算法、特征提取算法和分类算法。
Recently, for the study of Text Automatic classification technology, researchers mostly focus on the exploration and improvement of different classification algorithms.
目前，对于文本分类技术的研究，大多数研究者的精力主要放在各种不同分类方法的探索与改进上。
Secondly, the text studies the Statistical Learning Theory(STL) and Support Vector Machine(SVM)theory seriously, discusses multi-category classification algorithms of SVM.
其次，认真研究了统计学习理论的主要内容和SVM算法的基本原理，并且就SVM的多种多类别分类算法分别加以讨论。
In literature, there are very few discussions on the change of performance of source camera classification algorithms when test images are subjected to minor image processing.
现有文献中的源相机分类算法很少讨论测试图像在受到轻微图像处理后算法性能的变化。
However, classifying hyperspectral images only with traditional classification algorithms will result in low classification precision, data redundancy and great waste of resource.
但仅用传统分类算法对高光谱图像分类，会导致分类精度降低、空间数据冗余和资源的极大浪费。
However, performing classification quickly on multiple fields is known to be difficult, and the fast increase of packet arrival rate also put great pressure on the packet classification algorithms.
但是高速多维报文分类本身是一个公认的难以解决的问题，而且报文速率的快速增长给报文分类算法带来了巨大的压力。
This paper introduces a common distance algorithm based on vectorial space, and proposes novel distance and classification algorithms which orient the topic on the basis of concept-semanteme space.
本文介绍了基于向量空间的常用距离的算法，并在概念语义空间的基础上，提出一种面向主题的距离和分类的算法。
As to the multiple classification algorithms of large size rule set, we introduce a algorithms named recursive flow classification and this algorithms use the linear buffer to implement recursive map.
接着本文介绍了应用于大规模规则库的快速分类算法的解决方案——递归流分类，该算法是一种利用线性存储区分块递归映射的算法。
Advanced image processing algorithms assist operators with change detection, image classification, identifying anomalies and tracking patterns of activity over time.
先进的图像处理算法协助操作人员随时进行变化检测、图像分类、识别异常和跟踪活动模式。
This article discussed two data mining algorithms: the classification tree and clustering.
本文讨论了两种数据挖掘算法：分类树和群集。
Similarly, with machine learning algorithms, a common problem is over-fitting the data and essentially memorizing the training set rather than learning a more general classification technique.
同样，对于机器学习算法，一个通常的问题是过适合(原文为over -fitting，译者注)数据，以及主要记忆训练集，而不是学习过多的一般分类技术。
Decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm, the experiment results are given.
利用决策树算法对乳腺癌图像数据进行分类，实现了一个基于决策树算法的医学图像分类器，获得了分类的实验结果。
We deal with the inconsistency through classification accuracy, using heuristic algorithms we can get a set of minimal productive rules satisfying the given classification accuracy.
通过分类正确度有效处理了决策表的不一致性，采用启发式算法，挖掘出满足给定精确度的最简产生式规则知识。
According to these two algorithms, this thesis proposed a new gene function classification algorithm based on gene function tree.
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。
There are some various algorithms in data mining, and decision tree classification algorithm is the most popular one.
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。
Its study covers the features of computer vision, methodologies, classification of hardware, architectural features for vision algorithms and computational resources.
研究内容涉及计算机视觉任务的特征、设计方法、硬件系统的分类、视觉算法的体系特征以及视觉处理的计算资源等问题。
The final classification is accomplished by the algorithms based on the classification results achieved from the above steps.
根据以上的对分块的分类结果，用分类算法完成鞋印纹理的最终分类。
Comprehensive analysis and comparisons are given for several typical algorithms in supervised and unsupervised classification.
针对几种典型的有监督及无监督分类算法，进行了深入的分析和比较。
Comprehensive analysis and comparisons are given for several typical algorithms in supervised and unsupervised classification.
针对几种典型的有监督及无监督分类算法，进行了深入的分析和比较。
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