Abstract-- This paper introduces a convex formulation approach for the initialization of parameter estimation problems (PEP). The proposed method exploits the parameter-affine feat...
Julian Bonilla Alarcon, Moritz Diehl, Bart De Moor...
This paper proposes a guaranteed robust bounded-error distributed estimation algorithm. It may be employed to perform parameter estimation from data collected in a network of wire...
This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [1...
We describe a new iterative method for parameter estimation of Gaussian mixtures. The new method is based on a framework developed by Kivinen and Warmuth for supervised on-line le...
We propose a probabilistic segmentation scheme, which is widely applicable to some extend. Besides the segmentation itself our model incorporates object specific shading. Dependent...
Frequently in the physical sciences experimental data are analyzed to determine model parameters using techniques known as parameter estimation. Eliminating the effects of noise ...
: Markov models have been proposed for the classification of DNA-motifs using generative approaches for parameter learning. Here, we propose to apply the discriminative paradigm fo...
Jan Grau, Jens Keilwagen, Alexander E. Kel, Ivo Gr...
Many useful applications of simulation in computational cell biology, e.g. kinetic parameter estimation, Metabolic Control Analysis (MCA), and bifurcation analysis, require a large...
Complex environmental modeling often involves a large number of unknown physical and ecological parameters. Parameter estimation is one of the most difficult steps in many modelin...
–We propose a new control technique for force control on unknown environments. In particular, the proposed approach overcomes the need for precise estimation of environment param...