— A pseudo-reverse approach is presented in this paper to analyze the evolutionary behaviour of enzymes. It employs the standard model of Nei and Gojobori [1] in a generalized fo...
Learning by imitation has shown to be a powerful paradigm for automated learning in autonomous robots. This paper presents a general framework of learning by imitation for stochas...
This article presents an approach to estimating exercise energy expenditure based on acceleration measurements from a wrist-worn biaxial sensor. The method uses the linear mixed m...
In this paper we present a procedure to learn a topological model of Situated Public Displays from data of people traveling between these displays. This model encompasses the dista...
This paper presents an improved software estimation model, which uses to estimate developing effort of e-Learning's contents. This model is called the e-Learning courseware E...
We present a computational model of amygdala neural networks. It is used to simulate neuronal activation in amygdala nuclei at different stages of aversive conditioning experiments...
We propose a modification of the dynamic neural field model of Amari [1], aiming at reducing the simulation effort by employing spaceand frequency representations of the dynamic st...
Alexander Gepperth, Jannik Fritsch, Christian Goer...
We present a generative model for unsupervised coreference resolution that views coreference as an EM clustering process. For comparison purposes, we revisit Haghighi and Klein...
This paper presents a novel model of co-reference knowledge, which is based on the distinction of (i) a model of a common reality, (ii) a model of an agent's opinion about rea...
We present a generative probabilistic model for the topographic mapping of tree structured data. The model is formulated as constrained mixture of hidden Markov tree models. A nat...