Special Issue on Machine Learning

Machine Learning, as an important branch of AI (Artificial Intelligence), concerns the built and study of systems that can learn from information. Core content related to machine learning deals with the representation and generalization of data. In all machine learning systems, data and functions representation plays an inevitable role. As for generalization, it is the property that the learning system will perform well in unseen information instances.

Machine learning has been applied in a wide variety of tasks as well as applications. Facing with the information explosion and rapid development of AI, the research on machine learning will be more urgent and significative.    

In this special issue, we intend to invite front-line researchers and authors to submit original research and review articles on exploring machine learning. Potential topics include, but are not limited to:

• Statistical learning
• Neural network learning
• Learning through evolution (evolutionary algorithms)
• Reinforcement learning
• Inductive learning
• Evolutionary computation
• Multi-lingual knowledge acquisition and representation
• Machine learning and information retrieval
• Machine learning for bioinformatics and computational biology
• Feature extraction and classification
• Machine learning in game playing and problem solving
• Machine learning in intelligent virtual environments
• Machine learning in economics, business and forecasting applications
• Machine learning in medicine, bioinformatics and systems biology
• Machine learning in homeland security applications
• Machine learning in industrial and engineering applications
  

Authors should read over the journal’s Authors’ Guidelines carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

Please kindly notice that the “Special Issue” under your manuscript title is supposed to be specified and the research field “Special Issue - Machine Learning” should be chosen during your submission.

According to the following timetable:

Guest Editor:

For further questions or inquiries

Please contact Editorial Assistant at

jsea@scirp.org