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.
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