Multi-Domain learning (MDL) assumes that the domain labels in the dataset are known. However, when there are multiple metadata attributes available, it is not always straightforward to select a single best attribute for domain partition, and it is possible that combining more than one metadata attributes (including continuous attributes) can lead to better MDL performance. In this work, we prop...

SUMMARY This paper addresses the problem of learning Bayesian belief networks (BBN) based on the minimum description length (MDL) principle. First, we give a formula of description length based on which the MDL-based procedure learns a BBN. Secondly, we point out that the diierence between the MDL-based and Cooper and Herskovits procedures is essentially in the priors rather than in the approac...

In this extended abstract we present our current work concerning the development of minimum description length (MDL) principle based solutions for medical image segmentation problems. Our application is the interpretation of overlapping nuclei in histological images in terms of individual nuclei within a parametric shape family. MDL is a well suited approach for this application as MDL provides...

Minimum Description Length (MDL) shape modelling has recently established itself as a gold standard in optimal shape modelling. By end of 2004, available MDL packages are written in Matlab and/or incorrectly. The purpose of this technical report is to list the most critical problems that occurred during a C++ implementation of MDL–based 2D shape modelling.

The inference of gene regulatory networks is a core problem in systems biology. Many inference algorithms have been proposed and all suffer from false positives. In this paper, we use the minimum description length (MDL) principle to reduce the rate of false positives for best-fit algorithms. The performance of these algorithms is evaluated via two metrics: the normalized-edge Hamming distance ...

The minimum description length (MDL) principle states that one should prefer the model that yields the shortest description of the data when the complexity of the model itself is also accounted for. MDL provides a versatile approach to statistical modeling. It is applicable to model selection and regularization. Modern versions of MDL lead to robust methods that are well suited for choosing an ...

We have characterized the binding of trans-1-(2,5-dimethoxyphenyl)-3-[4-(dimethylamino)phenyl]-2-methyl-2- propen- 1-one (MDL 27048) to purified procine brain tubulin, and the inhibition of microtubule assembly by this compound in vitro and using cultured cells. Binding measurements were performed by difference absorption and fluorescence spectroscopy. MDL 27048 binds to one site/tubulin hetero...

The two most popular model selection rules in the signal processing literature have been the Akaike’s criterion AIC and the Rissanen’s principle of minimum description length MDL. These rules are similar in form in that they both consist of data and penalty terms. Their data terms are identical, but the penalties are different, the MDL being more stringent toward overparameterization. The AIC p...

BACKGROUND Tracheal intubation can be associated with considerable hemodynamic changes, particularly in patients with uncontrolled hypertension. The GlideScope(®) video-laryngoscope (GVL) is a novel video laryngoscope that does not need direct exposure of the vocal cords, and it can also produce lower hemodynamic changes due to lower degrees of trauma and stimuli to the oropharynx than a Macint...

Probabilistic programs provide an appealing language for describing mental theories, because they are Turing complete: any computable process may be described as a program. Program induction is the problem of inferring theories, in the form of (probabilistic) programs, that describe some set of observations. Minimum Description Length, or MDL, is one common approach to program induction [11]. T...