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Special Issue "Advanced Learning Methods for Complex Data"
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (15 October 2018) | Viewed by 11920
Special Issue Editors
Interests: natural language processing; semantic web
Interests: data mining and machine learning; high-dimensional data analysis; feature selection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Since the introduction of process modeling for knowledge discovery, the importance of data mining methods has increased dramatically, making this research area relevant and challenging to extract actionable knowledge from complex data. In recent years, new algorithms and machine learning methods have been experimented with to deal with domains that present multiple challenges including high-dimensionality, heterogeneity of features, and complex relationships between data objects.
Emerging approaches are showing the enormous benefits of learning from complex data, including text, video, audio and the large amount of information related to new research domains, such as big data, the Internet of things, cloud computing, etc., often available on the web according to multiple modalities, multiple resources and multiple formats. Many efforts in the machine learning community have been focused on these specialized types of data.
This Special Issue welcomes papers covering a wide range of topics in the area of learning from complex data, including the following areas of interest:
- Algorithms for advanced data analysis
- Data mining and knowledge discovery over complex data
- Platforms and data mining applications in all domains including social, web, bioinformatics and finance
- Text mining and natural language processing
- Machine learning and statistical methods for multimedia and graph data
- Learning methods for data streams and the Internet of things
- Big data analytics
We accept both research papers and case studies based on robust and strict methodology with a substantial proportion of original (not published elsewhere) content.
Prof. Maurizio Atzori
Prof. Barbara Pes
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. Information is an international peer-reviewed open access monthly 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.
- Machine Learning
- Data Mining
- Text Mining
- Statistical methods
- Big data