In this paper the application of reinforcement learning to Tetris is investigated, particulary the idea of temporal difference learning is applied to estimate the state value funct...
Ant-based clustering is a nature-inspired technique whereas stochastic agents perform the task of clustering high-dimensional data. This paper analyzes the popular technique of Lum...
In modern thermonuclear fusion devices it is possible to distinguish distinct types of plasma confinement regimes which have different performance in terms of confinement time. Dis...
Guido Vagliasindi, Paolo Arena, Luigi Fortuna, And...
Using multilayer perceptrons (MLPs) to approximate the state-action value function in reinforcement learning (RL) algorithms could become a nightmare due to the constant possibilit...
This work advances the Support Vector Machine (SVM) based approach for predictive modelling of failure time data as proposed in [1]. The main results concern a drastic reduction in...
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...
Input selection is an important consideration in all large-scale modelling problems. We propose that using an established noise variance estimator known as the Delta test as the ta...
Abstract. We consider the problem of learning an unknown (overcomplete) basis from an unknown sparse linear combination. Introducing the "sparse coding neural gas" algori...
Our goal is proposing an unbiased framework for gene expression analysis based on variable selection combined with a significance assessment step. We start by discussing the need ...
Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Ales...