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1，什么叫过采样

一般模数转换时会采用的，采样频率有要求，一般频率越大恢复的越好，反之亦然，我就知道这点

什么叫过采样

2，请教undersampling

undersampling,欠采样技术,我理解就是用较低的频率去采样较高的频率.
等出差回国有时间我会发点Analog的讲谈上来过几天，OK

请教undersampling

3，求问数字下采样跟数字下变频一样么

你问的是数字欠采样和数字下变频吧！这完全是两概念，数字下变频就是通过混频的方式，降低信号的载波频率，数字欠采样是相对耐奎斯特采样定律而言，当采样频率fs<2fc时，就是欠采样，反之为过采样。

求问数字下采样跟数字下变频一样么

4，带通采样的欠采样信号怎么进行恢复

欠采样信号把高频型号搬运到了低频，恢复的话涉及到频谱搬运和滤波，实际操作并不容易
欠采样时，信号采集量较少，失真大，不易还原；过采样时，信号采集量较多，失真小，容易还原；临界采样介于二者之间！

5，数字图像处理中为什么图像放大可以看做过采样缩小可以看做欠采样

图像放大是个数字图像处理的过程，常见的有插值方法。图像放大，使得图像的细节更清晰，用空间频率来描述的话，频率分辨率更高，比如放大前一个像素位置大小对应某个空间／角度分辨率，放大后，一个像素的大小对应的分辨率更小，更清晰，所以可以达到过采样。反之，缩小，也可以实现欠采样。只是这里的过采样和欠采样，是根据系统参数和处理要求而确定，不是肯定过了或者欠了。

6，数据挖掘中的分类问题

这叫resampling重采样，欠采样意思明白但这个词不合适如果要求减少多样本类的数据比例而不影响分类精度，没有其它信息的话重采样应该应尽可能保持该类的分布概率密度。有信息的话比如知道类分布模式或是分类面特征，可以参考SVM的思想重采样从分类边界开始向两侧密度渐降重采样。若能排序则直接取前90%即可。
Data mining can be used to uncover patterns in data but is often carried out only on samples of data. The mining process will be ineffective if the samples are not a good representation of the larger body of data. Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being "mined". Inability to find patterns may become a cause for some disputes between customers and service providers. Therefore data mining is not foolproof but may be useful if sufficiently representative data samples are collected. The discovery of a particular pattern in a particular set of data does not necessarily mean that a pattern is found elsewhere in the larger data from which that sample was drawn. An important part of the process is the verification and validation of patterns on other samples of data.The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques to sample sizes that are (or may be) too small for statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). Data dredging may, however, be used to develop new hypotheses, which must then be validated with sufficiently large sample sets
数据挖掘(data mining-dm)是从存放在数据库、数据仓库、或其它信息库中的大量数据中挖掘有趣知识的过程川。数据挖掘有时也称作kdd, kdd(knowledge discovery in databases-kdd:知识发现)即是基于数据库的知识发现，指的是从大型数据库或数据仓库中提取人们感兴趣的知识，这些知识是隐含的、事先未知的、潜在有用的、易被理解的信息。实质上，这两个概念的内涵大致相同，只是从不同的角度认识问题而已。譬如人工智能的研究人员倾向于讲kdd，而计算机和信息技术专家通常说数据挖掘。

文章TAG：采样  什么  过采样  欠采样  
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