| 1. | There are many ways for constructing classifier
有许多模型用来构造分类器。 |
| 2. | Classification is an important sub - branch of data mining , which aims to build the classifier used to predict the class label of new coming data
分类( classification )是数据挖掘领域的一个重要研究分支，分类首先要构造分类器，并对依据分类器对新数据进行类别预测。 |
| 3. | Mining classification rules is a procedure to construct a classifier through studying the training dataset . it is a very important part of data mining and knowledge discovery
分类规则挖掘则是通过对训练样本数据集的学习构造分类规则的过程，是数据挖掘、知识发现的一个重要方面。 |
| 4. | That is to say , how to make algorithm effective in large dataset is same to small dataset . this measure main idea is reduce primary occupation in order to improve the efficiency of algorithm
即如何使得算法对海量的数据集同小量的数据集一样具有很高的效率，减少内存的限制，提高构造分类器的效率。 |
| 5. | In the final , the thesis build a rapid face detection system using a cascaded classifiers based on adaboost learning algorithm , harr - like feature and the method of building classifier
最后本文运用基于adaboost学习算法和harr - like特征及本文提出的由特征构造分类器方法，采用一种分级分类器的结构，搭建了一个快速的人脸检测系统。 |
| 6. | Different from traditional classification machine , our research is preceded under the situation of lacking class label and class information , replacing manual classification with clustering in order to gain classification information and the rustle is good
与传统分类器不同，我们在缺乏类信息的情况下，采用聚类替代领域专家的人工分类获得类信息，为构造分类器提供合适的类信息，取得了较好效果。 |
| 7. | There are three factors must research in the rapid face detection method based on learning : learning algorithm , the definition of features and the method of building classifier based on these features , the manner of combining effectively these features
本文认为，在基于学习的快速人脸检测方法中，有三个需要研究的要素，分别是学习算法、特征的定义及基于此特征形式构造分类器的方法和特征的有效组合方式。 |
| 8. | ( 2 ) the influence to classification result is highly effected by using different classifier , for example , the center - vector algorithm obtains better classification results than other two algorithms . with the character feature , the average recall is 80 . 73 % , and the average precision is 82 . 94 % , and with the chinese - word feature , the average recall is 83 . 6 % , and the average precision is 85 . 97 % . different corpuses influence the classification result . for example , the average recall is 89 . 31 % and the average precision is 88 . 33 % , by using the news web pages as corpus from the web site " www . sina . com . cn " , which adopt the center - vector algorithm to structure classifier and select chinese - word as feature
对三种分类器分别以字、词为特征进行分类测试、分析发现：使用相同的分类算法，用词作为特征项，比以字作为特征的分类效果好；用不同的算法构造分类器对分类效果的影响很大，如中心向量算法在字、词特征下的分类效果优于其他两算法；在以字为特征的情况下，该算法的平均查全率80 . 73 ，平均查准率82 . 94 ；在以词为特征的情况下，该算法的平均查全率83 . 6 ，平均查准率85 . 97 ；选用语料不同对分类效果也有影响，如用新浪网( www . sina . com . cn )网页语料进行测试，使用中心向量法分类器和词作为特征的情况下，平均准确率为89 . 31 ，平均查全率为88 . 33 。 |
| 9. | The current moderately strong earthquakes in east china have been preliminarily classified according to the tectonics based on information from laboratory study results - it presents the similar fracture pattern and characteristics of precursory evolution to similar structural tectonics on the action of extra adding loads
摘要基于在相似外载荷作用下结构相似的构造可表现出类似的破裂图像及前兆演化特徵的实验室研究结果，对华东地区现代中强地震进行了初步构造分类，在此基础上分析研究了19次震例前地震活动图像异常的统计特徵。 |
| 10. | This paper proposed some methods for finding out sure regions and ambiguous regions defined by lower and upper approximations in rough set theory . an applicable ending - criterion for semi - supervised back - propagation algorithm was proposed and a new rough classifier framework was studied , the assessment results show the effectiveness of the proposed criterion . a new classifier based on support vector machines was studied and applied
本文提出了几种划分样本边界区的方法:提出了一种应用于半监督bp算法的实用结束判据，并根据粗糙集理论，研究了一种新的粗糙分类机制，取得良好的效果；应用支持向量机理论，构造分类器并划分样本边界区；最后研究多个分类器集成的方法寻找样本边界区，同样提高了暂态稳定评估的可靠性 |
