The deadline for submission to the Machine Learning Special Issue on Inductive Logic Programming is very close:
31 March 2017 The CFP follows... ____________________________________________________________ CALL FOR PAPERS Machine Learning Journal Special issue on Inductive Logic Programming ____________________________________________________________ We are delighted to announce an open call for a Machine Learning Journal special issue on Inductive Logic Programming. Papers for the special issue are solicited in all areas of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections to statistical learning and other probabilistic approaches. In addition to the above topics, we also encourage contributions in the areas of cognitive technologies, knowledge acquisition from big data, the cloud and crowd sourced data, deep relational learning, as well as contributions on the application of any of these solutions to real world problems. The papers can address topics including, but not limited to: - Theoretical aspects: logical-foundations of learning; computational/statistical learning theory; specialisation and generalisation; probabilistic logic-based learning; graph and tree mining. - Representation and languages for learning: logic programming; Datalog; first-order logic; description logic and ontologies; higher-order logic; Answer Set Programming; probabilistic logic languages; constraint logic programming; knowledge graphs. - Algorithms and systems: learning with (semi-)structured data; (semi-)supervised and unsupervised relational learning; relational reinforcement learning; predicate invention; propositionalisation approaches; multi-instance learning; learning in the presence of uncertainty; meta-level learning. - Applications of learning: art; bioinformatics; systems biology; games; medical informatics; robotics; natural language processing; web-mining; software engineering; modelling and adaptation of control systems; socio-technical systems. Paper Submission: Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. All papers will be reviewed following standard reviewing procedures for the Machine Learning journal. Papers must be prepared in accordance with the Journal guidelines: http://www.springer.com/10994 Manuscripts must be submitted to: http://MACH.edmgr.com <http://mach.edmgr.com/> An article is submitted to the ILP'16 special issue by choosing "S.I. : ILP 2016" as the article type. Articles should preferably be no longer than 20 pages, and submissions exceeding this length will not be given priority during reviews and will be under review for a longer period causing delays to the publication of the special issue. Important Dates: Submission deadline: 31 March 2017 First review results: 15 May 2017 Revised papers due: 17 July 2017 Final selection: 15 August 2017 The Special Issue Guest Editors: Alessandra Russo, Imperial College London James Cussens, University of York -- James Cussens Dept of Computer Science & York Centre for Complex Systems Analysis Room 326, The Hub, Deramore Lane Tel +44 (0)1904 325371 University of York Fax +44 (0)1904 500159 York YO10 5GE, UK http://www.cs.york.ac.uk/~jc http://www.york.ac.uk/docs/disclaimer/email.htm
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