分类资料

Nonparametric model is a reasonable alternative to the conventional binormal model when degenerate data sets occur or when the sample size of continuous data is large.

非参数法中Hanley和McNeil法计算相对简单，容易理解，适用于一般独立的有序分类资料与连续性资料。

In this research, the SOLO (Structure of the Observed Learning Outcome) Taxonomy presented by Australian scholars Biggs and Collis (1982) was adopted, and students studying the fifth and sixth grades in Taipei area were chosen as research targets.This study was primarily implemented with a quantitative analysis and supported by a qualitative analysis. In order to carry out the research, a questionnaire had been designed with criteria suggested by Jones et al.(2000), including "describing data","organizing and reducing data","representing data" and "analyzing and interpreting data." This questionnaire consisted of open-ended questions, and after analyzed with qualitative methods, the responses were classified and scored based on standards set in SOLO Taxonomy. Then the quantitative method "Partial Credit Analysis" from Rasch model was adopted in examining the reliability, validity and consistency of outcomes from the qualitative analysis.

提出的统计思维架构－「描述资料」、「组织及简化资料」、「呈现资料」、「分析资料」作为评量试题的向度，题型为开放性问答题，在区分学童的答题表现方面，采用质的分析，参照SOLO（Structure of the Observed Learning Outcome）分类法的分类标准，将学童不同的答题反应进行分类，给予不同的分数，最后再采用Rasch model中的部份给分模式进行量化分析，以量化数据佐证质性分析的信度与效度，以及不同给分的吻合度。

If outcome is continuous, it makes no statistical sense to dichotomize it.

如果结果是连续的，那么使其变成二分类资料是没有统计必要的

For organizing vegetation submodel, started with the bottom-up gateway to agglomerate plot-data. The vegetation types must be confirmed before predictive vegetation mapping, therefore designed a transforming program to combine 585 plot-data from different surveys for the purpose. But the result of the classification and ordination of the combined plot-data couldn't display comprehensive vegetation types of Taiwan, because the plots were insufficient and disproportionate. Consequently, this study adopted the top-down gateway to divisive vegetation. After referring to Taiwan and east-Asia vegetation research papers and American national vegetation classification system etc., the physiognomic classification scheme of Taiwan had been drafted as vegetation submodel. The scheme included 4 levels: 2 classes, 5subclasses (subarctic, coldtemperate, cooltemperate, warmtemperate, subtropical), 8 groups (based on phenology and types of predominant leaf) and 16 formations (based on moisture regime and predominant taxa). And, the scheme dealt completely with the diverse nomenclatures of vegetation types in the past.

为建立植群亚模型，首先依「整合大量样区资料，由下而上的低阶植相聚类」之途径，设计建档程序及转档程式，成功整并不同来源之585个样区资料，但因样区数目不足且分布不均，分析结果未能显示出台湾完整的植群类型；故转循「预先设定植群类型，由上而下的高阶形相分类」之途径，经参考他国植群分类系统及台湾、东亚植群分类研究报告，订定分类原则及架构，提出4阶层之潜在植群形相分类方案：(1)寒原、森林2类群系纲；(2)亚寒带、冷温带、凉温带、暖温带、亚热带5类群系亚纲；(3) 8类群系组表徵优势植群之叶片的物候与形态；(4) 16类群系说明其水分境制及优势分类群；此方案统一处理了过去各种有关植群划分之纷岐名制，方案之各阶植群类型亦即本研究之植群亚模型。

Thesis aim at the large and complicated toponyms in the maps, and make a symetic anti proper specification. According to above feature and map compilation, we can combine with asymbol data to the module of design the system of toponym standard specification and toponym database.

本文之研究针对地图中巨量、繁复的地名资料，研拟系统性之分类设计，以地名基本特性及编图原则为著眼点，并与地图符号资料相互配合，期能据以规划全国性地名资料分类体系之规范化模式。

As soon as you've installed Semantic Drilldown, you can go to the "ViewData" special page and see what the category structure looks like on your site. There you can see what filters are needed or would be helpful for each category.

钻取资料当您安装好本外挂后，请前往「查看资料」特殊页面，查看在您的站台内有那些分类及其结构，这样便於您了解该如何针对某些分类设定所需之筛选器。

This paper expatiated the characters about different kinds of Remote Sensing data and application ranges ,then analysed synthetically current primary image classification methods.On this base, the paper summarized routine statistic methods, artificial intelligence classificational methods , basing on factual condition to classify and else new methods,etc.Land use/cover ; remote sensing

本文阐述了不同类型遥感资料的性质及应用范围，并在综合分析研究当前主要的遥感图像分类方法基础上，对常规的统计分类方法、人工智能分类方法、针对实际情况的分类方法及其它较新的分类方法进行了综述。

In order to provide more information for the further taxonomic studies on Sterculiaceae, three problems are carefully analyzed here, which are (1) the taxonomic position and systematic relations of the family,(2) the taxonomic position and relationships of some genera in this family, and (3) the application of some new taxonomic characteristics to the classification of the family.

本文从梧桐科的分类地位和系统关系﹑属的分类地位和亲缘关系以及分类学新特征在梧桐科分类中的应用三个方面入手，分析了梧桐科分类学研究的历史﹑现状和存在的问题，特别是把来自分子资料的研究结果与传统分类进行了比较分析，试图为梧桐科的分类学研究提供更多的帮助。

In this paper the related data of underlying surface at home and abroad and the common criterions of land use classifications were utilized, the actual conditions were also combined and the underlying surface feature, including land use, soil feature and terrain elevation, over Heihe River and it's neighbourhood have been classified into two kinds of grided data with high resolution (about 3 km×3 km and 6 km×6 km respectively).

本文综合了国内外诸种有关资料，借鉴了国际上较通用的下垫面特征分类标准，也结合黑河地区下垫面的实际情况，对HEIFE试验区及其邻近地区的下垫面特征(包括地表特征、土壤特征及地形高度)进行归纳分类，形成了两种高分辨率(分别约为6km×6km及3km×3km)的下垫面特征的格点化资料，可供该地区地气相互作用研究用。

In a database the concept of an example might change along with time which is known as concept drift When the concept drift the classification model built by use of old data is unsuitable for classify new data Therefore concept drift has become a hot issue in data mining in recent years Although many algorithms had been proposed to resolve this problem they can not provide users with the reason of concept drift However a user might be very interested in such rules For example doctors want to find what makes disease change; researchers want to know the reason of the variety of the weather; and decision makers would like to understand why a customer's shopping habit change In this thesis we propose a Concept Drift Rule mining Tree called CDR-Tree to solve this problem CDR-Tree can not only find the rule of concept drift also build the prediction model for both old and new data at the same time

无论在大型资料库或现实生活中，同一资料样本的概念有可能会随著时间的递移而改变，也就是产生所谓的概念漂移。当样本发生概念漂移时，由旧有资料所建构的分类模组将不再适用於预测新获得的资料，因此，近年来概念漂移已成为资料探勘中一项热门的研究议题。虽然已有？多学者提出不同的技术来解决概念漂移的问题，但是这些方法都是利用修正或重建的方式来产生适合新资料的预测模组，并无法提供造成概念漂移的原因。然而对使用者而言，其感兴趣的可能正是这些引起概念漂移的规则，如医生可能想了解引起疾病变化的主因、学者会想要知道气候转变的规则、或是决策者想找出顾客购物习惯改变的因素等。因此，本论文提出概念漂移规则探勘树( Concept Drift Rule mining Tree )，简称CDR-Tree，来解决这个问题。CDR-Tree不但能探测出造成概念漂移的主要原因，亦能同时建立新旧资料的预测模组以供决策者运用使用。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。