Theme:
Recent advances in storage, hardware, and networking have resulted in a large amount of web data. This has powered the demand to extract useful and actionable insights from such complex and large-scale datasets in an automatic, reliable and effective way. Machine learning, which aims to construct algorithms that can learn from and make predictions on data intelligently, has attracted increasing attention in the recent years and has been successfully applied to many web data mining tasks, such as user behavior modeling, social media computing, online recommendation, link analysis, etc. Since a lot of machine learning algorithms formulate the learning tasks as linear, quadratic or semi-definite mathematical programming problems, optimization becomes a crucial tool and plays a key role in machine learning and web data mining tasks. On the other hand, machine learning and the applications in web data mining are not simply the consumers of optimization technology, but a rapidly evolving interdisciplinary research field that is itself promoting new optimization ideas, models, and solutions.
This special session "Advanced Methods in Optimization and Machine Learning for Web Data Mining" aims to provide a platform for academics and industry-related researchers in the areas of applied mathematics, machine learning, pattern recognition, data mining, knowledge management, network science, social media, and big data to exchange ideas and explore traditional and new areas in optimization and machine learning as well as their applications in webdata mining.
Scope and Topics:
The topics of this special session include, but are not limited to:
Agent and autonomyoriented
computation
Cloud-based computing
Clustering and graph-partitioning
for web data
Collaborative and content based
filtering
Context aware optimization
Cross-media learning
Crowd behavior analysis
Distributed/parallel optimization
algorithms in machine learning
EM algorithm and alternating
optimization
Feature and subspace selection for
web data abstractions
Graph-based learning for web/network
data
Human-agent interaction
Implementation issues of
optimization and learning in web data mining
Intelligent agents on the web
Learning and adaptation in
Multi-agent Systems
Learning complex social networks
Learning for imbalanced web data
Learning for personalization,
advertising, and recommendation in web data
Learning for user behavior modeling
Multimedia search and retrieval on
web
Multi-objective optimization and
many-objective optimization
Non-convex optimization and
numerical methods in machine learning
Optimization and machine learning in
crowdsourcing
Optimization for large-scale web
data
Optimization for mobile computing
Optimization in evolutionary
computation
Probabilistic models and graphical
models for web data mining
Regularization and generalization in
machine learning
Security of web data mining
Sequential learning for video and
audio data on the web
Social and economic agents
Social media mining
Sparse coding for web data mining
Supervised/semi-supervised/unsupervised
learning for web data mining
Support vector machines and kernel
methods for web data mining
Visualization for high-dimensional
web data
Online Paper Submission:
Importance Date:
Paper
Submission Due Date: June 1, 2016
Paper
Notification Date: June 25, 2016
Organizers:
Prof. Yiu-ming Cheung
Department of Computer Science, Hong Kong Baptist University, Hong Kong
E-mail: ymc(at)comp.hkbu.edu.hk
Dr. Yang Liu
Department of Computer Science, Hong Kong Baptist University, Hong Kong
E-mail: csygliu(at)comp.hkbu.edu.hk
