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Special Issue "Learning with Big Data: Scalable Algorithms and Novel Applications"
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (31 January 2018) | Viewed by 22006
Special Issue Editors
Interests: big data applications; parallel algorithms; clustering; machine learning algorithms
Interests: stochastic optimization; online optimization; deep learning; big data analytics
Special Issue Information
Recent years have witnessed unprecedented growth in the scale, dimensionality and complexities of data in various areas, spurring BIG DATA research and development. Big data research has empowered the success of many applications in urban computing, social science, e-commerce, computer vision, natural language processing, speech recognition, bioinformatics, education, physics, chemistry, biology, and engineering. On the other hand, in order to enable learning with big data, scalable algorithms have attracted much attention in machine learning and data mining. Numerous computational techniques for Big Data have been proposed, including stochastic optimization, parallel and distributed optimization, randomization, and GPU computing. This Special Issue addresses the emerging topic of learning with big data, with an emphasis on novel applications and scalable algorithms. Papers may choose to mainly focus on one aspect (novel applications or scalable algorithms) but also provide sufficient background or discussion on the other.
Topics of Interest
Topics of interest include but not limited to:
- Novel Applications of Machine Learning and Data Mining on Big Data. In particular, we welcome novel applications in spatial and temporal data mining, urban and mobile computing, smart city and smart community, e-commerce, and computer vision.
- Big Data Learning Techniques and their applications. These include stochastic optimization, online optimization, parallel and distributed optimization, randomized dimensionality reduction, GPU computing, etc. The employment of these techniques for solving machine learning and data mining problems on big datasets are particularly welcome.
- Novel Machine Learning Methods such as Deep Learning and their applications to big datasets.
Prof. Dr. Suely Oliveira
Dr. Tianbao Yang
Dr. Xun Zhou
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access quarterly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- big data
- novel applications
- scalable algorithms
- deep learning
- stochastic optimization
- spatio-temporal data mining