Special Issue "Machine Learning for Big Data Analytics"

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A special issue of Machines (ISSN 2075-1702).

Deadline for manuscript submissions: closed (30 September 2014)

Special Issue Editor

Guest Editor
Dr. João Gama
Laboratory of Artificial Intelligence and Decision Support and Faculty of Economics, University of Porto, Porto, Portugal
Website: http://www.liaad.up.pt/area/jgama//
E-Mail: jgama@fep.up.pt
Phone: +351 222 094 000
Fax: +351 222 094 050
Interests: machine learning; data mining; learning from data streams; learning from massive data; ubiquitous data mining

Special Issue Information

Dear Colleagues,

A recent report from the McKinsey Global Institute refers to machine learning as the boosting methodology for the next frontier for innovation, competition, and productivity. As a silent technology, machine learning is used in network analysis, health, search, spam filtering, recommender systems, ad placement, credit scoring, fraud detection, stock trading, energy demand prediction, and many other applications. This special issue focuses on current machine learning techniques to deal with large volumes of heterogeneous data. We aim at providing a forum for the presentation of new algorithmic developments and applications of machine learning and data mining for big data. We welcome theoretical, applied and visionary papers pointing to next generation machine learning algorithms and techniques for big data. We especially welcome challenging real world applications of machine learning. This special issue aims to reflect the state of the art in challenges faced by machine learning developers and to present the most important and relevant advances to overcome these challenges.

We welcome papers in:

Methods and Applications of Machine Learning
Machine Learning Methods for Big Data
Analysis of Massive Graphs
Distributed Data Mining
Real-Time and Real-World Applications

Position papers and state of the art reviews are especially welcome.

Dr. João Gama
Guest Editor

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. For the first couple of issues the Article Processing Charge (APC) will be waived for well-prepared manuscripts. English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.


Keywords

  • machine learning
  • data mining
  • learning from data streams
  • learning from big data
  • learning from networked data
  • learning from graph data
  • real-world applications of machine learning
  • distributed data mining

Published Papers

No papers have been published in this special issue yet.

Last update: 29 November 2013

Machines EISSN 2075-1702 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert