Structured Machine Learning for Mapping Natural Language to Spatial Ontologies KU Leuven
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Because billions of euros in research and development investment are needed to successfully bring a new drug to the market, tools that improve the drug candidate selection process have a significant pharmaceutical impact. Machine learning methods have already proven to be very useful in order to move forward in this field. However data availability is often the limiting factor. Therefore the focus in our research is to develop and evaluate ...
Machine learning has great application potential in healthcare. Yet many healthcare applications have data characteristics that make it difficult to apply standard machine learning methods. For example, patient outcomes can often be expressed in different ways and are difficult to summarize in one value. This requires the application of so-called structured output learning methods. Such methods are not yet widely used in practice, because it ...
State of the art computer vision systems are fundamentally reliant on statistical learning to optimize performance on a specific application. Currently, statistical frameworks in computer vision are typically based on classification and regression, probabilistic graphical models, or discriminative structured prediction frameworks such as the Structured Output Support Vector Machine (SOSVM). Although some of the best performing computer vision ...
State of the art computer vision systems are fundamentally reliant on statistical learning to optimize performance on a specific application. Currently, statistical frameworks in computer vision are typically based on classification and regression, probabilistic graphical models, or discriminative structured prediction frameworks such as the Structured Output Support Vector Machine (SOSVM). Although some of the best performing ...