We construct machine learned regressors to predict the behaviour of DNA sequencing data from the fluorescent labelled Sanger method. These predictions are used to assess hypothes...
Dynamic programming is introduced to quantize a continuous random variable into a discrete random variable. Quantization is often useful before statistical analysis or reconstruct...
Mingzhou (Joe) Song, Robert M. Haralick, Sté...
Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate, while increasing the estima...
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
We propose a novel approach for on-line treatment verification using cine EPID (Electronic Portal Imaging Device) images for hypofractionated lung radiotherapy based on a machine ...
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
The basic idea of Lucas and Kanade is to constrain the local motion measurement by assuming a constant velocity within a spatial neighborhood. We reformulate this spatial constrai...
Volker Willert, Julian Eggert, Marc Toussaint, Edg...
High dimensionality of belief space in Partially Observable Markov Decision Processes (POMDPs) is one of the major causes that severely restricts the applicability of this model. ...
Abdeslam Boularias, Masoumeh T. Izadi, Brahim Chai...