This paper attempts to answer a central question in unsupervised learning: what does it
mean to “make sense” of a sensory sequence? In our formalization, making sense involves …

Abstract Machine learning methods are growing in relevance for biometrics and personal
information processing in domains such as forensics, e health, recruitment, and e learning …

Machine learning methods are growing in relevance for biometrics and personal information
processing in domains such as forensics, e-health, recruitment, and e-learning. In these …

Learning from interpretation transition (LFIT) automatically constructs a model of the
dynamics of a system from the observation of its state transitions. So far the systems that …

Modeling biological regulation mechanisms breaks down into two main trends. The first,
quantitative, is based on ordinary differential equations involving the quantitative expression …

La modélisation des mécanismes de régulation biologique se décompose en deux
principales tendances. La première, quantitative, repose sur les équations différentielles …

We propose the identification of feedback mechanisms in biological systems by learning
logical rules in R. Thomas' Kinetic Logic (Thomas and D'Ari in Biological feedback. CRC …

Learning from interpretation transition (LFIT) automatically constructs a model of the
dynamics of a system from the observation of its state transitions. So far the systems that …

LFIT is a well known declarative machine learning framework able to generate propositional
logic twins of complex systems. It needs discrete input data. It has been successfully applied …

Marine ecology models are used to study and anticipate population variations of plankton
and microalgae species. These variations can have an impact on ecological niches, the …