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Introduction

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.

Call for paper

Important date

2016-06-01
Draft paper submission deadline
2016-06-25
Draft paper acceptance notification

Submission Topics

  • 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

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Important Date
  • Conference Date

    Oct 13

    2016

    to

    Oct 16

    2016

  • Jun 01 2016

    Draft paper submission deadline

  • Jun 25 2016

    Draft Paper Acceptance Notification

  • Oct 16 2016

    Registration deadline

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