对于具有多模糊特征变量的多分类问题，自动提取适当的模糊模式识别规则集至关重要。
It's important to extract an appropriate fuzzy rules set for multi-classification problems that have fuzzy variables.
采用了一种基于编码支持向量机的多分类方法，该方法解决了SVM多分类问题的同时，有效地减少了训练和测试时间，提高了算法的效率。
We solve the multi-classification task by using a so called Coding SVM. By using this algorithm, we not only solve the classification task but also reduce the training and testing time.
针对空战目标识别中机型识别这一问题，提出了基于多分类器融合的识别方法。
Aiming at aircraft type recognition in the field of automatic target recognition, a method based on combining classifiers is proposed.
多分类器联合是解决复杂模式识别问题的有效办法。
A combination of multiple classifiers is a powerful solution to the difficult pattern recognition problem.
分析了多分类器融合算法的理论框架，并采用决策模板算法对蛋白质结构类的预测问题进行了研究。
We investigate the theoretical framework of multiple classifiers fusion, and apply the decision template algorithms to classify the protein secondary structural classes.
本文针对文本区域提取这个问题来进行研究，包含预处理、多分辨分析、特征提取、分类(检测)、区域提取五个步骤来解决文本区域的准确提取问题。
In this thesis, we study on text detection. It includes five parts: pre-process, multi-scale analysis, feature extraction, classification and text area extraction.
提出一种基于多分类器协同训练的遥感图像检索方法，该方法在不同特征集上分别建立分类器，利用不同分类器的协同性自动标记未知样本，从而有效解决了小样本问题。
There are usually few training samples in the tasks of content-based remote sensing image retrieval, which will lead to over-learning problem while using this small data set for training.
本文研究基于凸风险最小化方法的多分类贪婪算法，推广二分类的学习问题到多分类的情形。
In this paper, learning algorithm for solving multi-category classification using convex upper losses is studied.
本文利用多分类支持向量机实现了国画图像的五分类问题。
This paper implements the issues of TCP five-classifying using multiple classifiers.
多分类器联合是解决复杂模式识别问题的有效办法。
A combination of multiple classifiers is a powerful solution to difficult pattern recognition problem.
多分类器组合是解决复杂模式识别问题的有效办法。
Multiple classifiers ensemble is an effective method to solve complex classification problems in pattern recognition field.
第三章，研究了基于不同模式特征的多分类器联合方法问题。
In chapter 3, the method of multi classifier combination based on different pattern features is studied.
而基于模糊规则的模式识别方法是一类可理解性好的非线性方法，但迄今为止还没有被应用于多分类器融合问题中。
As a nonlinear method, the fuzzy rule-based pattern recognition has good comprehensibility, but has not been applied to the multiple classifier fusion.
而基于模糊规则的模式识别方法是一类可理解性好的非线性方法，但迄今为止还没有被应用于多分类器融合问题中。
As a nonlinear method, the fuzzy rule-based pattern recognition has good comprehensibility, but has not been applied to the multiple classifier fusion.
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