机构

• 长春光学精密机械与物... [1]
• 自动化研究所 [1]

采集方式

• OAI收割 [2]

内容类型

• 会议论文 [1]
• 学位论文 [1]

发表日期

• 2013 [1]
• 2012 [1]

学科主题

筛选

浏览/检索结果: 共2条，第1-2条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 大类别集分类与自适应及其在汉字识别中的应用 学位论文  OAI收割 工学博士, 中国科学院自动化研究所: 中国科学院大学, 2013 作者:  张煦尧 收藏  |  浏览/下载：56/0  |  提交时间：2015/09/02 大类别集模式分类  手写汉字识别  降维  局部平滑  修正二次判别函数  分类器自适应  风格迁移映射  模式域分类  Large Category Classification  Handwritten Chinese Character Recognition  Dimensionality Reduction  Local Smoothing  MQDF  Adaptation  Style Transfer Mapping  Pattern Field Classification   On hyperspectral remotely sensed image classification based on MNF and AdaBoosting (EI CONFERENCE) 会议论文  OAI收割 2012 3rd IEEE/IET International Conference on Audio, Language and Image Processing, ICALIP 2012, July 16, 2012 - July 18, 2012, Shanghai, China 作者:  Yu P.;  Yu P.;  Gao X. 收藏  |  浏览/下载：23/0  |  提交时间：2013/03/25 As an effective statistical learning tool  AdaBoosting has been widely used in the field of pattern recognition. In this paper  a new method is proposed to improve the classification performance of hyperspectral images by combining the minimum noise fraction (MNF) and AdaBoosting. Because the hyperspectral imagery has many bands which have strong correlation and high redundancy  the hyperspectral data are pre-processed by the minimum noise fraction to reduce the data's dimensionality  whilst to remove noise bands simultaneously. Then  we use an AdaBoost algorithm to conduct the classification of hyperspectral remotely sensed image. Experimental results show that the classification accuracy is improved and the time of calculation is reduced as well. 2012 IEEE.