| 1. | Efficiency analysis is to prove ulac an efficient model itself . significance analysis is
有效性的分析,是验证ulac本身是一个无指导学习环境下的属性选择方法。 |
| 2. | Learning contract is just a way to stimulate students to achieve these purposes
学习合同正是这样一种能让学生自我指导学习，使他们能够主动参与教育过程的学习模式。 |
| 3. | As we all know , the methods of feature selection for supervised learning perform pretty well with strong practice and simple operation
众所周知,在有指导学习环境下,出现了很多性能优越、实用性强和操作方便的属性选择方法。 |
| 4. | According to various of applications of the datasets , feature selection algorithms can be categorized as either supervised learning or unsupervised learning feature selection approaches
属性选择问题可以分为有指导学习环境下的选择和无指导学习环境下的选择。 |
| 5. | Provide guidance for learning e . g . presentation of content is different from instructions on how to learn . should be simpler and easier that content . use of different channel
指导学习：比如，教学内容的呈现跟教学方式是不同的。应该尽量通过不同的渠道将教学内容简单化呈现。 |
| 6. | The typical ones include relief - f , information gain and chi - square etc . feature selection was considered as feature selection in supervised learning from traditional view
其中的典型代表有relief - f 、信息增益和卡方检验等。过去传统意义上的属性选择通常是指在有指导学习环境下的属性选择。 |
| 7. | 8th joint symp . computational linguistics , nanjing , 2005 , pp . 217 - 220 . 54 wang j b , du c l , wang k z . study of automatic abstraction system based on natural language understanding
国内研究者探索了基于实例无指导学习方法互信息计算词汇向量空间基于依存分析与贝叶斯分类模型结合等各种方法。 |
| 8. | Ec employs a coding technology and some genetic operations . under the pressure of selection , which means " fits survive " , the algorithm can produce an optimal solution
它采用简单的编码技术来表示各种复杂的结构，并通过对编码进行简单的遗传操作和优胜劣汰的自然选择来指导学习和确定搜索的方向。 |
| 9. | There are few systematic approaches for building the learning organization and measuring the organizational learning capability , so implementing organizational learning is not an easy task
在组织学习研究领域，能够系统地指导学习型组织创建和测量组织学习能力的方法很少，因此很难把组织学习理论应用于实践。 |
| 10. | The point of paper is to make a deep survey on feature selection for unsupervised learning , which can provide some valuable practical experience of enhancing efficiency of data mining for unsupervised learning
本文的重点就是对无指导学习下的属性选择进行深入研究,以此为无指导学习环境下的提高数据挖掘的效率提供一些实践经验。 |
