Generalised max entropy classifiers

In this paper we propose a generalised maximum-entropy classification framework, in which the empirical expectation of the feature functions is bounded by the lower and upper expectations associated with the lower and upper probabilities associated with a belief measure. This generalised setting permits a more cautious appreciation of the information content of a training set. We analytically derive the KarushKuhn-Tucker conditions for the generalised max-entropy classifier in the case in which a Shannon-like entropy is adopted

The shortest path problem on networks with fuzzy parameters

Shortest path problem where the costs have vague values is one of the most studied problems in fuzzy sets and systems area. However, due to its high computational complexity, previously published algorithms present peculiarities and problems that need to be addressed (e.g. they find costs without an existing path, they determine a fuzzy solution set but do not give any guidelines to help the decision-maker choose the best path, they can only be applied in graphs with fuzzy non-negative parameters, etc.). In this paper, one proposes an iterative algorithm that assumes a generic ranking index for comparing the fuzzy numbers involved in the problem, in such a way that each time in which the decision-maker wants to solve a concrete problem (s)he can choose (or propose) the ranking index that best suits that problem. This algorithm, that solves the above remarked drawbacks, is based on the Ford-Moore-Bellman algorithm for classical graphs, and in concrete it can be applied in graphs with negative parameters and it can detect whether there are negative circuits. For the sake of illustrating the performance of the algorithm in the paper, it has been here developed using only certain order relations, but it is not restricted at all to use these comparison relations exclusively. The proposed algorithm is easy of understanding as the theoretical base of a decision support system oriented to solving this kind of problems. (c) 2007 Elsevier B.V. All rights reserved.158141561157

Clinical Diagnostic Biomarkers from the Personalization of Computational Models of Cardiac Physiology

Computational modelling of the heart is rapidly advancing to the point of clinical utility. However, the difficulty of parameterizing and validating models from clinical data indicates that the routine application of truly predictive models remains a significant challenge. We argue there is significant value in an intermediate step towards prediction. This step is the use of biophysically based models to extract clinically useful information from existing patient data. Specifically in this paper we review methodologies for applying modelling frameworks for this goal in the areas of quantifying cardiac anatomy, estimating myocardial stiffness and optimizing measurements of coronary perfusion. Using these indicative examples of the general overarching approach, we finally discuss the value, ongoing challenges and future potential for applying biophysically based modelling in the clinical context.</p