IEEE Transactions on Pattern Analysis and Machine Intelligence
宗旨和范围
IEEE Transactions on Pattern Analysis and Machine Intelligence (sometimes abbreviated as IEEE PAMI or simply PAMI) is a monthly peer-reviewed scientific journal published by the IEEE Computer Society. It covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition. The editor-in-chief is Sven J. Dickinson (University of Toronto). According to the Journal Citation Reports, the journal has a 2020 impact factor of 16.389. Less
关键指标
期刊详情
- 出版商IEEE COMPUTER SOC
- 出版语言English
- 出版频率Monthly
- 出版语言English
- 出版频率Monthly
- 创刊年份1979
- Publisher URL
- 网址
Topics Covered
年度发行情况
- 5Y
- 10Y
常见问题
IEEE Transactions on Pattern Analysis and Machine Intelligence 是从何时开始发行的？ 
IEEE Transactions on Pattern Analysis and Machine Intelligence 自1979开始发行至今。
IEEE Transactions on Pattern Analysis and Machine Intelligence 多久发行一次？
IEEE Transactions on Pattern Analysis and Machine Intelligence 为Monthly。
IEEE Transactions on Pattern Analysis and Machine Intelligence 的出版商是谁？
IEEE Transactions on Pattern Analysis and Machine Intelligence 的出版商是IEEE COMPUTER SOC。
我在哪里查看 IEEE Transactions on Pattern Analysis and Machine Intelligence 的宗旨和范围？
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我如何在意得辑上查看IEEE Transactions on Pattern Analysis and Machine Intelligence 的指标？
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IEEE Transactions on Pattern Analysis and Machine Intelligence 的 eISSN和pISSN 号分别是什么？
1939-3539 的 eISSN 为 0162-8828，pISSN 为 IEEE Transactions on Pattern Analysis and Machine Intelligence 。
该期刊重点关注哪些主题？
本期刊关注的主题范围广泛，包括 Self-regulated learning, Deep learning, Point cloud, Single image, Activity recognition, Hash function, Missing data, Image restoration, Block design, Light field, Gaussian process, Global optimization, Structural similarity, Architecture, Stiefel manifold, Machine learning, Image segmentation, Backbone network, Feature learning, Data structure。
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