Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Non-Parametric Learning for Monocular Visual Odometry

This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bayesian regression technique, the Gaussian Process (GP), is used to elect the most probable transformation function hypothesis from input to output, based on training data collected prior and during navigation. Other than eliminating the need for a geometric model and traditional camera calibration, this approach also allows for scale recovery even in a monocular configuration, and provides a natural treatment of uncertainties due to the probabilistic nature of GPs. Several extensions to the traditional GP framework are introduced and discussed in depth, and they constitute the core of the contributions of this thesis to the machine learning and robotics community. The proposed framework is tested in a wide variety of scenarios, ranging from urban and off-road ground vehicles to unconstrained 3D unmanned aircrafts. The results show a significant improvement over traditional visual odometry algorithms, and also surpass results obtained using other sensors, such as laser scanners and IMUs. The incorporation of these results to a SLAM scenario, using a Exact Sparse Information Filter (ESIF), is shown to decrease global uncertainty by exploiting revisited areas of the environment. Finally, a technique for the automatic segmentation of dynamic objects is presented, as a way to increase the robustness of image information and further improve visual odometry results

Toward Probabilistic Analysis of Guidelines

Contains fulltext : 92503.pdf (author's version ) (Open Access)ECAI 2010 Workshop KR4HC 2010, 17 augustus 201

Using bayesian networks in an industrial setting: Making printing systems adaptive

Contains fulltext : 83711.pdf (preprint version ) (Open Access)ECAI 2010, 16 augustus 201

Modelling the interactions between discrete and continuous causal factors in Bayesian networks

Contains fulltext : 83806.pdf (preprint version ) (Open Access)PGM 2010, 13 september 201

On the Application of Formal Methods to Clinical Guidelines, an Artificial Intelligence Perspective

Contains fulltext : 66828.pdf (publisher's version ) (Open Access)In computer science, all kinds of methods and techniques have been developed to study systems, such as simulation of the behaviour of a system. Furthermore, it is possible to study these systems by proving formal formal properties or by searching through all the possible states that a system may be in. The formal notation and techniques that are used in the development of digital systems are referred to by the term `formal methods'. Clinical guidelines are documents that include recommendations, advice, and management instructions aimed at supporting the decision-making in healthcare. Medicine is a field in which mistakes made have a major bearing on the health and life expectancy of people, so it is important to make sure that the recommendations in these clinical guidelines are sound. In this thesis, the use of formal methods for quality-checking clinical guidelines is investigated.RU Radboud Universiteit Nijmegen, 18 april 2008Promotor : Weide, Th.P. van der Co-promotor : Lucas, P.J.F

From Probabilistic Logic to Chain Logic

Contains fulltext : 72527.pdf (preprint version ) (Open Access)BNAIC 2008, 30 oktober 200

Checking Guideline Conformance of Medical Protocols using Modular Model Checking

Contains fulltext : 36051.pdf (preprint version ) (Open Access)BNAIC'0

Argumentation Systems for History--Based Construction of Medical Guidelines

Contains fulltext : 33245.pdf (preprint version ) (Open Access

Qualitative chain graphs and their use in medicine

Contains fulltext : 103657.pdf (publisher's version ) (Open Access)PGM 2012: 6th European Workshop on Probabilistic Graphical Models, Granada (Spain), 19-21 September, 201