Call for papers: the 28th IJCNN 2016 Special Session on Advanced Machine Learning Methods and Applications from Complicated Data Environment, July 25-29, 2016, Vancouver, CANADA
AIMS AND SCOPE
Traditional machine learning methods have been commonly used for many applications, such as text classification, image recognition, and video tracking. For learning purposes, this data is often required to be represented as vectors. However, many other types of data objects in real-world applications contain rich feature vectors and structural information, such as chemical compounds in bio pharmacy, brain regions in brain networks and users in social networks. Simple feature-vector representation inherently loses the structural information of the objects. In reality, objects may have complicated characteristics, depending on how the objects are assessed and characterized, or the data may come from heterogeneous domains, such as traditional tabular-based data, sequential patterns, social networks, time series information, and semi-structured data. Novel machine learning methods are desired to discover meaningful knowledge in advanced applications from objects with complicated characteristics.
This special session expects to solicit contributions on advanced machine learning methods and applications to complicated data environments.
The topics of interest include, but are not limited to:
- Supervised/Unsupervised/Semi-supervised Learning
- Semi-structured Learning
- Graph-based Learning
- Graph Classification/Clustering/ Streaming
- Multi-Graph Learning
- Deep Graph Learning
- Online Graph Learning
- Time Series Learning
- Complex Social Networks
- Multi-view/instance/ label Learning
- Heterogeneous Transfer Learning
- Web/Text/Image Mining
- Multimedia Learning
- Paper submission: 15 January 2016.
- Notification of paper acceptance: 15 March 2016.
- Camera-ready deadline: 15 April 2016.
- Conference days: 25-29 July 2016.
All papers must be submitted through the IEEE IJCNN 2016 online submission system. For special session papers, please make sure you select the respective special session title “Advanced Machine Learning Methods and Applications from Complicated Data Environment” under the list of research topics in the submission system. For manuscript style information, you can refer to paper submission/templates/style at the conference website.
Papers submitted for special sessions are to be peer-reviewed with the same criteria used for the rest of contributed papers. As a result, all accepted papers will be included in the main conference proceedings of IEEE IJCNN 2016.
SPEICAL SESSION CHAIRS
Jia Wu QCIS Centre, University of Technology, Sydney, Australia Jia.Wu@uts.edu.au
Shirui Pan QCIS Centre, University of Technology, Sydney, Australia Shirui.Pan@uts.edu.au
Peng Zhang QCIS Centre, University of Technology, Sydney, Australia Peng.Zhang@uts.edu.au
Xingquan Zhu Florida Atlantic University, USA firstname.lastname@example.org
Chengqi Zhang QCIS Centre, University of Technology, Sydney, Australia Chengqi.Zhang@uts.edu.au
Philip S. Yu University of Illinois at Chicago, USA email@example.com
More information about this Special Session can be found the attached pdf here.