United Kingdom

One of the main assumptions of classical machine learning is that data are generated by a stationary concept. This is often violated in practice if unexpected events happen, sensor drift is present, of models should be individualized for a specific user, for example. In such situations, a number of challenges occur: How to detect that drift occurs? How to adapt the model on the fly, given new data, without catastrophic forgetting? How to incorporate measures which can mediate suboptimal behavior? In the talk, we will address three algorithmic challenges: flexible drift detection with minimum requirements, life-long model adaptation, and reject options. We will demonstrate the behavior of the algorithmic models in the context of medical applications and industry4.0, respectively.

IJCCI 2021

Machine Learning in Non-Stationary Environments

The purpose of WINSYS, the International Conference on Wireless Networks and Mobile Systems, is to bring together researchers, engineers and practitioners interested on information systems and applications in the context of wireless networks and mobile technologies. Information systems and information technology are pervasive in the whole communications field, which is quite vast, encompassing a large number of research topics and applications: from practical issues to the more abstract theoretical aspects of communication; from low level protocols to high-level networking and applications; from wireless networking technologies to mobile information systems; many other topics are included in the scope of WINSYS.

WINSYS 2021

The purpose of WINSYS, the International Conference on Wireless Networks and Mobile Systems, is to bring together researchers, engineers and practitioners interested on information systems and applications in the context of wireless networks and mobile technologies.

keynote

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation. 

In this talk, some research topics that are worth exploring (from the personal perspective of the speaker) in evolutionary multi-objective optimization will be briefly discussed. Such topics include scalability of multi-objective evolutionary algorithms (both in objective and in decision variable space), indicator-based selection, hyper-heuristics, parallelism and scalarizing functions. In the final part of the talk, some personal thoughts about the future of the field will also be briefly discussed.

Premium content

Machine Learning in Non-Stationary Environments

Downloads

Next from IJCCI 2021

Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal Perspective

Similar lecture

Efficient Deep Learning Methods for IoT Applications: Current Challenges and Future Directions

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES