分类机

New model utilizes grid computing technology,plans as a whole of network computing resources,combines with statistic classification advantage of support vector machine in finite sample space,probes into application of grid computation technology in image classification.

为了充分利用网格技术分布式、高性能、协同共享的能力，设计了一种基于网格和支持向量机的分布式图像分类器模型，采用网格计算技术，统筹网络运算资源，结合支持向量机在有限样本统计分类中的优势，探索网格技术在图像分类中的应用。

In the first approach the metadata extraction is viewed as a classification problem. Three statistic classification models, including Support Vector Machines, Perceptron with Uneven Margins, and Maximum Entropy are studied. The approach suffers from the problems of ignoring the variance in lengths of documents and ignoring the dependency of text units within documents. To solve the problems, our second approach takes the metadata extraction as a sequence data labeling problem and makes full use of the sequential information.

论文首先研究了基于分类的元数据抽取方法，考察了支持向量机、非对称间隔感知机和最大熵模型在ADME中的应用；然后针对分类方法中存在的忽略文档之间的差异、不考虑文档上下文等问题，提出了基于序列数据标注模型的抽取方法，并结合最大熵马尔可夫模型、投票感知机模型和条件随机场模型进行了研究；最后本文根据前两种方法的设计思想和优缺点，研究提出一种新的结构预测模型—— CDASP（Coordinate Descent Algorithm for Structure Prediction），并将其应用于元数据的自动抽取中。

After comparing Bayes,K neighbors,neural network and support vector machine these four typical text categorization devices,the author builds a text categorization system using support vector machine.

在比较了贝叶斯、K近邻、神经网络和支持向量机四种典型的文本分类器的基础上，采用支持向量机分类器搭建了一个文本分类试验系统。

This paper firstly reports three kinds of technique of text automatic category based on statistic ( k nearest neighbor ,support vector machine and nave bayes),and analyses their advantages and disadvantages.

本文首先介绍三种基于统计的自动分类技术(k近邻分类器、支持向量机分类器和朴素贝叶斯分类器)，剖析了基于统计的自动分类的优势及不足。

This paper firstly reports three kinds of technique of text automatic category based on statistic (k nearest neighbor, support vector machine and nave bayes), and analyses their advantages and disadvantages.

本文首先介绍三种基於统计的自动分类技术（k近邻分类器、支持向量机分类器和朴素贝叶斯分类器），剖析了基於统计的自动分类的优势及不足。

The parallelepiped classifier,minimum distance classifier,Maximum Likelihood Classifier,Neural network and,especially,the newly developed Support Vector Machines were assessed in the object-based image analysis of VHSR data. The impacts of kernel configuration on the performance of the SVM and of the selection of training data of the four classifiers were also evaluated.

对比研究了平行六面体、最近邻分类法、最大似然法、神经网络等经典分类算法以及近年来新发展的支持向量机分类算法在基于分割对象的高分辨率遥感图像分类中的性能，详细分析了不同内积核函数对于支持向量机分类的影响。

Regarding to the multi-granularity of and analysis demand of stock index futures transaction data, this article proposed a classification method based on support vector machines. Tangent plane solution of support vector machines is established by introducing geometrical solution. Therefore, investors can be classified based on straightforward transaction data.

针对股指期货成交数据的多粒度性以及成交数据分析的需求，本文提出在解决噪声、多粒度数据分类问题方面有突出优越性的支持向量机分类方法，引入几何求解方式，提出了支持向量机的切平面解法，根据成交数据直观地对投资者进行分类。

The design of vehicle type classification based on Support Vector Machine is collecting characteristic parameters provided to segregator after vehicle image preprocessing and feature extraction,and classifying the type of vehicle by using technique of Support Vector Machine and binary decision tree according to vehicle image from video camera.

基于支持向量机的车型分类的设计思路是通过视频采集获得车辆图像，对车辆图像进行图像预处理和特征提取后，得到分类器所需数据特征，而后采用支持向量机和二元决策树对车型分类。

Many polarimetric target decompositions were explored, and support vector machine was applied to the classification of polarimetric SAR image. A new algorithm of target classification was presented based on polarimetric target decomposition and support vector machine. Finally, the effect of classification results was discussed by selecting different kernels and different parameters.

五系统地对各种极化目标分解方法进行了深入的探讨，并应用支持向量机对极化SAR图像进行分类研究，提出了一种基于极化目标分解和支持向量机的目标分类算法，并通过选择各种类型的核函数以及不同的参数，探讨其对地物目标分类结果的影响。

The traditional classification methods only use one single classifier, which may lead to onesidedness, low accuracy, and that the samples nearby the Support Vector Machine hyperplanes are more easily misclassified. To solve these problems, the multifeature fusion method based on SVM and KNearest Neighbor classifiers was presented in this paper.

针对传统分类方法只采用一种分类器而存在的片面性，分类精度不高，以及支持向量机分类超平面附近点易错分的问题，提出了基于支持向量机和k近邻的多特征融合方法。

For a big chunk of credit-card losses; the number of filings (and thus charge-off rates) would be rising again, whether

年美国个人破产法的一个改动使得破产登记急速下降，而后引起了信用卡大规模的亏损。

Eph. 4:23 And that you be renewed in the spirit of your mind

弗四23 而在你们心思的灵里得以更新