In some applications, especially spectrometric ones, curve classifiers achieve better performances if they work on the m-order derivatives of their inputs. This paper proposes a sm...
In this paper we define and address the problem of safe exploration in the context of reinforcement learning. Our notion of safety is concerned with states or transitions that can ...
This paper presents a new approach for time series prediction using local dynamic modeling. The proposed method is composed of three blocks: a Time Delay Line that transforms the o...
We investigate the effect of several adaptive metrics in the context of figure-ground segregation, using Generalized LVQ to train a classifier for image regions. Extending the Euc...
Alexander Denecke, Heiko Wersing, Jochen J. Steil,...
The aim of this paper is to enhance the performance of a reinforcement learning game agent controller, within a dynamic game environment, through the retention of learned informati...
Driven by the growing demand of personalization of medical procedures, data-based, computer-aided cancer research in human patients is advancing at an accelerating pace, providing ...
Alfredo Vellido, Elia Biganzoli, Paulo J. G. Lisbo...
This paper addresses the possible use of virtual neural sensors, implemented by means of weightless systems, as active or reactive sensors. The latter, made possible by the intrins...
This paper presents a bio-inspired model for visual perception of motion through its principal indicator : the neuromimetic motion indicator (NMI). This indicator emerges out of th...
In this paper a novel procedure to select the input nodes in neural network modeling is presented and discussed. The approach is developed in a multiple testing framework and so it...