与两种基于免疫原理的文本分类方法和传统的贝叶斯分类器进行了比较研究。
It is compared with two kinds of classifier, which is based on the principle of immunity, and traditional Bayesian classification.
概述了光纤光栅传感器解调原理，并从响应特性分类角度对光纤光栅的几种解调方法进行了分析比较。
The demodulation principle of FBG is outlined, and several demodulation methods are analyzed and compared from the classification of response characteristics.
用这样一个模糊规则来表示分类的模糊系统，更加有效地构建了一个能对训练样本比较准确分类的模糊分类器。
This fuzzy system design method that uses a fuzzy rule to represent a cluster is then propsed so that a fuzzy classifier can be efficiently constructed to correctly classify the training data.
通过对基于多项式核函数和径向基核函数的支持向量机分类器进行比较，并且得到三种肝脏分类的识别率。
The classification accuracy of SVM based on polynomial and radial basis function kernel were compared, and the recognition accuracy of the three categories were obtained.
在UCI标准数据集合上进行测试，与airs和其他传统分类器进行比较，目的是研究基于人工免疫网络原理的数据分类方法的性能。
It is tested on the UCI standard data sets and compared with AIRS and the other classical classifiers. The aim is to research the performance of classifier based on artificial immune network.
通过比较几种常用调制分类器的优缺点，提出了一种新的调制分类器设计方案。
A new design of modulation classifier is proposed based on comparisons to the advantages and disadvantages of some commonly used modulation classifiers.
该方法通过几个分类器间协同学习，选出标记可信度比较高的无标记数据，再利用这些数据对已有的分类器作进一步的改进。
This method utilizing co-learning among several classifiers, selects the unlabeled samples which have high confidence, and then refines each classifier with these samples.
针对此问题，本研究对结合支持向量机（SVM）算法的几种常用非平衡数据分类方法进行实验比较，包括投票整合分类器和移动分类面等。
Combined with support vector machine (SVM) algorithm, several common approaches to deal with the unbalanced problem were compared, including majority voting, the moving boundary surface, etc.
在分类器设计环节，比较五种核非线性分类器，并根据宽带极化雷达目标散射数据的特点，使用融合分类的方法对目标进行分类。
In classification stage, five kernel-based classifications are used and compared, and fusion methods are designed for wide-band polarimetric radar target classification.
在分类器设计环节，比较五种核非线性分类器，并根据宽带极化雷达目标散射数据的特点，使用融合分类的方法对目标进行分类。
In classification stage, five kernel-based classifications are used and compared, and fusion methods are designed for wide-band polarimetric radar target classification.
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