图 书 馆
基于EO学习的土地覆盖分类特征选择Feature Selection in Land-Cover Classification using EO-learn |
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| 课程网址: | http://videolectures.net/sikdd2019_kenda_feature_selection/ |
| 主讲教师: | Klemen Kenda |
| 开课单位: | Jožef Stefan研究所人工智能实验室 |
| 开课时间: | 2019-11-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Applying machine learning to Big Data can be a cumbersome task which requires a lot of computational power and memory. In this paper we present a feature selection technique for land-cover classiffication in earth observation scenario. The technique extends the state-of-the-art feature extractors by pruning the dimensionality of the required feature space and can achieve almost optimal results with 10-fold reduction of the number of features. The approach utilizes a genetic algorithm for generation of optimal feature vector candidates and multi-objective optimization techniques for candidate selection. |
| 关 键 词: | 基于EO学习; 土地覆盖分类; 分类特征选择 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-09-14：cyh |
| 最后编审: | 2022-09-19：cyh |
| 阅读次数: | 21 |