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This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. Is allows to output a valid probability interval. The methodology is designed for...
Inductive conformal predictors have been designed to overcome the computational inefficiency exhibited by conformal predictors for many underlying prediction algorithms. Whereas computationally efficient, inductive conformal predictors sacrifice different parts of the training set at different...
Detection of anomalous trajectories is an important problem for which many algorithms based on learning of normal trajectory patterns have been proposed. Yet, these algorithms are typically designed for offline anomaly detection in databases and are insensitive to local sub-trajectory anomalies....
This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters. Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic...
Indoor localisation is the state-of-the-art to identify and observe a moving human or an object inside a building. However, because of the harsh indoor conditions, current indoor localisation systems remain either too expensive or not accurate enough. In this paper, we tackle the latter issue in...
QSAR modeling is a method for predicting properties, e.g. the solubility or toxicity, of chemical compounds using machine learning techniques. QSAR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity during...
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