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wy123wy123

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[求助] 群智能算法和数据挖掘类论文投稿期刊推荐求助已有1人参与

关于群智能算法和数据挖掘的论文，正文非中文单词数约7千左右，论文有算法模型、算法仿真比较和测试分析。请问可以哪些投相关的SCI期刊？要求审稿周期短，因子暂不特别要求（相对容易些的），检索较稳定，最好不是开源期刊（费用太高了），请各位推荐几个(简单说明下审稿周期及推荐说明)

[ Last edited by wy123wy123 on 2014-2-24 at 15:43 ]

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1楼 2014-02-24 14:01:15

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lightning0

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引用回帖:

7楼: Originally posted by wy123wy123 at 2014-02-26 21:28:01
这个符合哪些条件否？...

IEEE TPAMI: http://www.computer.org/portal/web/tpami/about

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a scholarly archival journal published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope. This includes all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Areas such as machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition and relevant specialized hardware and/or software architectures are also covered.

IEEE TKDE: http://www.computer.org/portal/web/tkde/about

IEEE Transactions on Knowledge and Data Engineering (TKDE) is an archival journal published monthly. The information published in this journal is designed to inform researchers, developers, managers, strategic planners, users, and others interested in state-of-the-art and state-of-the-practice activities in the knowledge and data engineering area. We are interested in well-defined theoretical results and empirical studies that have potential impact on the acquisition, management, storage, and graceful degeneration of knowledge and data, as well as in provision of knowledge and data services. We welcome treatments of the role of knowledge and data in the development and use of information systems and in the simplification of software and hardware development and maintenance. Since the journal is archival, it is assumed that the ideas presented are important, have been well analyzed or empirically validated, and are of value to the knowledge and data engineering research community. Specific topics include, but are not limited to: (a) knowledge discovery and data mining, (b) data modeling and management, (c) underlying computational platforms for knowledge and data engineering tools, techniques and systems, and (d) emerging knowledge and data engineering applications.