Real-time outlier detection for large datasets by RT-DetMCD

Modern industrial machines can generate gigabytes of data in seconds, frequently pushing the boundaries of available computing power. Together with the time criticality of industrial processing this presents a challenging problem for any data analytics procedure. We focus on the deterministic minimum covariance determinant method (DetMCD), which detects outliers by fitting a robust covariance matrix. We construct a much faster version of DetMCD by replacing its initial estimators by two new methods and incorporating update-based concentration steps. The computation time is reduced further by parallel computing, with a novel robust aggregation method to combine the results from the threads. The speed and accuracy of the proposed real-time DetMCD method (RT-DetMCD) are illustrated by simulation and a real industrial application to food sorting

Real-time discriminant analysis in the presence of label and measurement noise

Quadratic discriminant analysis (QDA) is a widely used classification technique. Based on a training dataset, each class in the data is characterized by an estimate of its center and shape, which can then be used to assign unseen observations to one of the classes. The traditional QDA rule relies on the empirical mean and covariance matrix. Unfortunately, these estimators are sensitive to label and measurement noise which often impairs the model's predictive ability. Robust estimators of location and scatter are resistant to this type of contamination. However, they have a prohibitive computational cost for large scale industrial experiments. We present a novel QDA method based on a recent real-time robust algorithm. We additionally integrate an anomaly detection step to classify the most atypical observations into a separate class of outliers. Finally, we introduce the label bias plot, a graphical display to identify label and measurement noise in the training data. The performance of the proposed approach is illustrated in a simulation study with huge datasets, and on real datasets about diabetes and fruit

Wishful thinking? Kant over de moeizame verhouding tussen ethiek en politiek in Zum ewigen Frieden

status: publishe

Human Rights, after 60 Years. A philosophical reflection

status: publishe

Wishful thinking? Kant over de moeizame verhouding tussen ethiek en politiek in Zum ewigen Frieden

status: publishe

Eindverslag project aanmoedigingsfonds. Wijzer met de jaren. Optimalisering van de leeromgeving voor werkstudenten wijsbegeerte 2008-2009

nrpages: 128status: publishe

Eindverslag OOI project [2005/13]. Eigen-wijs: individuele training wijsgerige vaardigheden 2005-2007

nrpages: 57status: publishe

Electrical stimulation in the bed nucleus of the stria terminalis alleviates severe obsessive-compulsive disorder

In 1998, we proposed deep brain stimulation as a last-resort treatment option for patients suffering from severe, treatment-resistant obsessive-compulsive disorder (OCD). Here, 24 OCD patients were included in a long-term follow-up study to evaluate the effects of electrical stimulation in the anterior limbs of the internal capsule (ALIC) and bed nucleus of the stria terminalis (BST). We find that electrical stimulation in the ALIC/BST area is safe and significantly decreases obsessions, compulsions, and associated anxiety and depressive symptoms, and improves global functioning in a blinded crossover trial (n=17), after 4 years (n=18), and at last follow-up (up to 171 months, n=24). Moreover, our data indicate that BST may be a better stimulation target compared with ALIC to alleviate OCD symptoms. We conclude that electrical stimulation in BST is a promising therapeutic option for otherwise treatment-resistant OCD patients.status: publishe