Machine Learning on Big Data (MLBD 2016) in conjunction with 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2016)
December 18-20, 2016
Anaheim, CA, USA

Call For Papers

The Special Session “Machine Learning on Big Data” (MLBD 2016) of the 15th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2016) focuses on machine learning models, techniques and algorithms related to Big Data, a vibrant and challenging research context playing a leading role in the Machine Learning and Data Mining research communities. Big data is gaining attention from researchers, being driven among others by technological innovations (such as cloud interfacss) and novel paradigms (such as social networks). Devising and developing machine learning models, techniques and algorithms for big data represent a fundamental problem stirred-up by the tremendous range of critical applications incorporating machine learning tools in their core platforms. For example, in application settings where big data arise and machine is useful, we recognize, among other things: (i) machine-learning-based processing (e.g., acquisition, knowledge discovery, and so forth) over large-scale sensor networks introduces important advantages over classical data-management-based approaches; similarly, (ii) medical and e-heath information systems usually include successful machine learning tools for processing and mining very large graphs modelling patient-to-disease, patient-to-doctor, and patient-to-therapy networks; (iii) genome data management and mining can gain important benefits from machine learning algorithms.Some hot topics in machine learning on big data include: (i) machine learning on unconventional big data sources (e.g., large-scale graphs in scientific applications, strongly-unstructured social networks, and so forth); (ii) machine learning over massive big data in distributed settings; (iii) scalable machine learning algorithms; (iv) deep learning – models, principles, issues; (v) machine-learning-based predictive approaches; (vi) machine-learning-based big data analytics; (vii) privacy-preserving machine learning on big data; (viii) temporal analysis and spatial analysis on big data; (ix) heterogeneous machine learning on big data; (x) novel applications of machine learning on big data (e.g., healthcare, cybersecurity, smart cities, and so forth).

The MLBD 2016 special session focuses on all the research aspects of machine learning on Big Data. Among these, an unrestricted list includes:

Topics

• Fundamentals
• Modelling
• Statistical Approaches
• Novel Paradigms
• Innovative Techniques
• Algorithms
• Innovative Architectures (GPU, Clouds, Clusters)
• Non-Conventional Big Data Settings (e.g., Spatio-Temporal Big Data, Streaming Big Data, Graph Big Data, Cloud Big Data, Probabilistic Big Data, Uncertain Big Data)
• Systems
• Architectures
• Advanced Topics (e.g., Dimensionality Reduction, Matrix Approximation Algorithms, Multi-Task Learning, Semi-Supervised Learning, Integration with NoSQL Databases)
• Case Studies and Applications

 

 

 

IMPORTANT DATES

Paper submission: August 06, 2016

           

Notification of acceptance: August 12, 2016

           

Final manuscripts due: September 05, 2017

                                  

User Name : prince
Posted 26-07-2016 on 12:20:03 AEDT

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