WCCI 2016 Special Session on

Advanced Methods in Optimization and

Machine Learning for Multimedia Computing

  

Scope

This special session "Advanced Methods in Optimization and Machine Learning for Multimedia Computing" aims to provide a platform for academics and industry-related researchers in the areas of applied mathematics, machine learning, artificial intelligence, pattern recognition, data mining, multimedia processing, and big data to exchange ideas and explore traditional and new areas in optimization and machine learning as well as their applications in multimedia computing. The topics of the special session include, but are not limited to:

Approximation algorithms
Cloud-based multimedia computing
Clustering and graph-partitioning for multimedia computing
Cross-media learning
Distributed/parallel optimization algorithms in machine learning
EM algorithm and alternating optimization
Extreme learning machines for multimedia computing
Feature and subspace selection for multimedia data abstractions
Graph-based learning for multimedia networks
High-dimensional data visualization
Human/crowd behavior analysis via machine learning
Implementation issues of optimization and learning in multimedia computing
Learning complex social networks
Learning for imbalanced multimedia data
Learning mechanisms of visual computing
Learning on brain-imaging data
Learning video content from unmanned aerial vehicle
Media content security
Metrics and methods to evaluate multimedia quality of experience
Mobile multimedia computing
Multimedia search and retrieval
Multi-objective optimization and many-objective optimization
Nonconvex optimization and numerical methods in machine learning
Optimization and machine learning in crowdsourcing
Optimization for deep models
Optimization for large-scale multimedia computing
Optimization in evolutionary computation
Optimization in statistics, statistical/computational tradeoffs
Optimization on manifolds, metric spaces
Optimization with sparsity constraints
Probabilistic models and graphical models for multimedia computing
Regularization and generalization in machine learning
Supervised/semi-supervised/unsupervised learning for multimedia computing
Sequential learning for video and audio data
Social media
SSupport vector machines and kernel methods for multimedia computing