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Bibliography
Currently, Dr. Reinders is active
in the field of Machine Learning. Besides studying fundamental issues, he
applies machine learning techniques to the areas of bioinformatics, computer
vision and context-aware recommender systems. His special interest goes
towards understanding complex systems (such as biological systems) that are
severely under-sampled. Issues that he studies are, amongst others,
constraining or reducing the complexity of models, the influences on and
necessary adaptations of learning algorithms when dealing with extremely
low number of training samples, and modeling of highly irregular classes.
His specific expertise in bioinformatics research covers the analysis and
interpretation of high-throughput screening devices (mainly micro-arrays).
He has made contributions to topics like clustering, genetic network
modeling and molecular classification.
Before, Dr. Reinders was active on
Computer Vision research. In particular, he studied the use of AI
techniques for Computer Vision problems, like motion estimation,
(model-based) image segmentation, and interpretation techniques, like
(supervised/unsupervised) pattern recognition, knowledge representation,
and reasoning strategies. These activities have given him a specific
expertise on the use model and/or knowledge information in computer
vision. He specifically concentrated on the concept of combining different
analysis techniques, i.e. data/information fusion, and he had a
specialization in (human) face related image processing.
As part of his PhD study he
investigated model-based coding in the context of video telephony. Besides
basic knowledge on image and video coding this research has given him
expertise on next generation coding paradigms. Specifically, he looked at
estimating the (static and dynamic) parameters of three-dimensional models
from video data. His MSc subject was on developing a new way to
geometrically represent three-dimensional (polyhedral) objects using aspect
graphs. Together with the modeling aspects within his PhD study, this has
given him also experience in the field of Computer Graphics.
Current fields of interest: Machine Learning, Bioinformatics, Computer Vision,
Data Mining, Recommender Systems and Man-Machine Interfacing.
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