为了提高文本分类的准确性，研究并设计了一个基于潜在语义分析和支持向量机的多类文本分类模型。
A multiclass text categorization model based on latent semantic analysis and support vector machine is researched and designed to enhance the accuracy of categorization.
而现实世界中的大部分数据都是多类数据，所以需要对简单支持向量机作进一步扩展，使之能解决多值分类问题。
In the real world most of the data is multi-class data, so the simple SVM need for further expansion, so that it can solve the multi-value classification.
该方法采用纠错编码支持向量机的多类分类技术，降低了经验风险，能对误差进行自动修正，有效地提高了识别率和识别速度。
A kind of error correction coding methods in the communication between singlechip′s serialport and PC in the electric spark forming machine control system is proposed.
介绍了几种常用的支持向量机多类分类方法，分析其存在的问题及缺点。
The problems and defections of the existing methods of SVM multi-class classification were analyzed. A multi-class classification based on binary tree was put forward.
介绍了支持向量机的变形算法、多类分类算法及模型选择问题；
The transformative algorithm based on SVM, multi-class SVM and model selection are also presented.
首先利用模板匹配进行粗分类，将多类问题转化成两类问题，再利用支持向量机进行精确分类。
In the proposed method, the original problem is firstly question firstly coarsely classified into a two-classes problem by using template matching, and then are accurately classified by means of SVM.
首先利用模板匹配进行粗分类，将多类问题转化成两类问题，再利用支持向量机进行精确分类。
In the proposed method, the original problem is firstly question firstly coarsely classified into a two-classes problem by using template matching, and then are accurately classified by means of SVM.
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