公开数据集
数据结构 ? 59M
Data Structure ?
* 以上分析是由系统提取分析形成的结果,具体实际数据为准。
README.md
Data Set Information:
The data sets we propose to analyse are constituted of 1024 vectors, each vector includes 10 parameters. You can think of it as a 1024*10 matrix. To produce these vectors, we proceed as follows:
1. we start with two 512*512 AVHRR images (1 in the visible, 1 in the IR)
2. each images is divided in super-pixels 16*16 and in each super-pixel we compute a set of parameters:
(a) visible: mean, max, min, mean distribution, contrast, entropy, second angular momentum
(b) IR: mean, max, min
The set of 10 parameters we picked to form the vectors is a compromised between various constraints. Actually we are still working on the choice of parameters for the data vectors. The data set I send you has not been normalized. The normalization of the data set is required by our classification scheme but that may not be true for yours. To normalize the data we compute the mean and standard deviation for each parameter on the entire data set then for each parameter of each vector we compute:
Norm. value = (un-norm value - mean)/SD
where
mean = mean value for this particular parameter over the data set
SD = standard deviation .....
Attribute Information:
N/A
Relevant Papers:
N/A
Papers That Cite This Data Set1:
Kristiaan Pelckmans and Jos De Brabanter and J. A. K Suykens and Bart De Moor and K. U. Leuven - ESAT. The Differogram: Non-parametric Noise Variance Estimation and its Use for Model Selection. SCDSISTA. 2004. [View Context].
Stephen D. Bay. Nearest neighbor classification from multiple feature subsets. Intell. Data Anal, 3. 1999. [View Context].
Cesar Guerra-Salcedo and Stephen Chen and Darrell Whitley and Sarah Smith. Fast and Accurate Feature Selection Using Hybrid Genetic Strategies. Department of Computer Science Colorado State University. [View Context].
C. esar and Cesar Guerra-Salcedo and Darrell Whitley. Feature Selection Mechanisms for Ensemble Creation : A Genetic Search Perspective. Department of Computer Science Colorado State University. [View Context].
Citation Request:
Please refer to the Machine Learning Repository's citation policy
帕依提提提温馨提示
该数据集正在整理中,为您准备了其他渠道,请您使用
- 分享你的想法
全部内容
数据使用声明:
- 1、该数据来自于互联网数据采集或服务商的提供,本平台为用户提供数据集的展示与浏览。
- 2、本平台仅作为数据集的基本信息展示、包括但不限于图像、文本、视频、音频等文件类型。
- 3、数据集基本信息来自数据原地址或数据提供方提供的信息,如数据集描述中有描述差异,请以数据原地址或服务商原地址为准。
- 1、本站中的所有数据集的版权都归属于原数据发布者或数据提供方所有。
- 1、如您需要转载本站数据,请保留原数据地址及相关版权声明。
- 1、如本站中的部分数据涉及侵权展示,请及时联系本站,我们会安排进行数据下线。