Henegar, Corneliu, Tordjman, Joan, Achard, Vincent, Lacasa, Daniele, Cremer, Isabelle, Guerre-Millo, Michele, ...
ABSTRACT: BACKGROUND: Investigations performed in mice and humans have acknowledged obesity as a low-grade inflammatory disease. Several molecular mechanisms have been convincingly involved in...
Henegar, Corneliu, Tordjman, Joan, Achard, Vincent, Lacasa, Daniele, Cremer, Isabelle, Guerre-Millo, Michele, ...
ABSTRACT: BACKGROUND: Investigations performed in mice and humans have acknowledged obesity as a low-grade inflammatory disease. Several molecular mechanisms have been convincingly involved in...
Henegar, Corneliu, Tordjman, Joan, Achard, Vincent, Lacasa, Danièle, Cremer, Isabelle, Guerre-Millo, Michèle, ...
Abstract Background Investigations performed in mice and humans have acknowledged obesity as a low-grade inflammatory disease. Several molecular mechanisms have been convincingly shown to be involved...
Bredeche, Nicolas, Shi, Zhongzhi, Zucker, Jean-Daniel
This paper deals with the possible benefits of Perceptual Learning in Artificial Intelligence. On the one hand, Perceptual Learning is more and more studied in neurobiology and is now considered as...
Bredeche, Nicolas, Shi, Zhongzhi, Zucker, Jean-Daniel
This paper deals with the possible benefits of Perceptual Learning in Artificial Intelligence. On the one hand, Perceptual Learning is more and more studied in neurobiology and is now considered as...
Henegar, Corneliu, Cancello, Raffaella, Rome, Sophie, Vidal, Hubert, Clément, Karine, Zucker, Jean-Daniel
MOTIVATION: Functional profiling is a key step of microarray gene expression data analysis. Identifying co-regulated biological processes could help for better understanding of underlying biological...
Henegar, Corneliu, Cancello, Raffaella, Rome, Sophie, Vidal, Hubert, Clément, Karine, Zucker, Jean-Daniel
MOTIVATION: Functional profiling is a key step of microarray gene expression data analysis. Identifying co-regulated biological processes could help for better understanding of underlying biological...
Kaput, Jim, Ordovas, José, Ferguson, Lynnette, Van Ommen, Ben, Rodríguez, Raymond, Allen, Lindsay, ...
Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotype and, conversely, how genes and their products metabolise these constituents into...
Bredeche, Nicolas, Shi, Zhongzhi, Zucker, Jean-Daniel
This paper deals with the possible benefits of Perceptual Learning in Artificial Intelligence. On the one hand, Perceptual Learning is more and more studied in neurobiology and is now considered as...
Perceptual Learning and Abstraction in Machine Learning (2003)
Nicolas Bredeche, Shi Zhongzhi, Jean-daniel Zucker
This paper deals with the possible benefits of Perceptual Learning in Artificial Intelligence. On the one hand, Perceptual Learning is more and more studied in neurobiology and is now considered as...
Abstracting Visual Percepts (2003)
Jean-daniel Zucker, Nicolas Bredeche, Lorenza Saitta
To e#ciently identify properties from its environment is an essential ability of a mobile robot who needs to interact with humans.
Online Learning for Object Identification By a Mobile Robot (2003)
Nicolas Bredeche, Jean-daniel Zucker, Shi Zhongzhi
Object identification for a situated robot is a first step towards many relevant behaviours such as human-robot communication, object tracking, object detection, etc. However, the dynamic and...
A Meta-Learning Approach to Ground Symbols from Visual Percepts (2003)
Nicolas Bredeche, Yann Chevaleyre, Jean-daniel Zucker, Alexis Drogoul
There is a growing interest in both the robotics and AI communities to give autonomous robots the ability to interact with humans. To eciently identify properties from its environment (be it the...
Abstracting Visual Percepts to Learn Concepts (2002)
Jean-daniel Zucker, Nicolas Bredeche, Lorenza Saitta
To efficiently identify properties from its environment is an essential ability of a mobile robot who needs to interact with humans. Successful approaches to provide robots with...
Stress response function of a two-dimensional ordered packing of frictional beads (2002)
Breton, Laurent, Claudin, Philippe, Clément, Éric, Zucker, Jean-Daniel
We study the stress profile of an ordered two-dimensional packing of beads in response to the application of a vertical overload localized at its top surface. Disorder is introduced through the...
Thse de doctorat s Informatique (2002)
Marie Curie, Paris Vi, Jean-gabriel Ganascia, Jean-daniel Zucker, ...
avail. Merci pour son soutien et ses remarques toujours pertinentes et constructives. Merci Franois Lecordix, de l'IGN, pour son travail sur le formidable outil d'exprimentation PlaGe, pour son...
Speeding up Recommender Systems with Meta-prototypes (2002)
Byron Bezerra, Geber L. Ramalho, Jean-Daniel Zucker
Recommender Systems use Information Filtering techniques to manage user preferences and provide the user with options, which will present greater possibility to satisfy them. Among these techniques,...
Noise-Tolerant Rule induction from Multi-Instance data (2001)
Yann Chevaleyre, Jean-daniel Zucker
This paper addresses the issue of multipleinstance induction of rules in the presence of noise. It first proposes a multiple-instance extensions of rule-based learning algorithms. Then, it shows what...
A Framework for Learning Rules from Multiple Instance Data (2001)
Yann Chevaleyre, Jean-daniel Zucker
This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. In a multiple-instance representation, each learning example is represented by a "bag" of...
Abstraction and Phase Transitions (2001)
Lorenza Saitta, Jean-daniel Zucker
this paper we are interested in the matching problem, i.e., satisfiability of a given First Order Logic formula on a set of universes of interpretation. This problem, fundamental in any complex...
Jean-daniel Zucker, Sastien Mustilr E, Lorenza Saitta
This article proposes a machine learning approach to overcome the knowledge acquisition bottleneck that limits the automation of cartographic generalisation. It first explains why this automation...
Jean-daniel Zucker, Yann Chevaleyre
In recent work, Dietterich et al. (1997) have presented the problem of supervised multiple-instance learning and how to solve it by building axis-parallel rectangles. This problem is encountered in...
Learning Structurally Indeterminate Clauses (2001)
Jean-daniel Zucker, Jean-gabriel Ganascia
. This paper describes a new kind of language bias, S-structural indeterminate clauses, which takes into account the meaning of predicates that play a key role in the complexity of learning in...
Noise-Tolerant Rule induction from Multi-Instance data (2001)
Yann Chevaleyre, Jean-daniel Zucker
This paper addresses the issue of multiple-instance induction of rules in the presence of noise. It first proposes a multiple-instance extensions of rule-based learning algorithms. Then, it shows...
Cartographic Generalization as a Combination of Representing and Abstracting Knowledge (2001)
Sbastien Mustire, Jean-daniel Zucker, Lorenza Saitta
This article shows that cartographic generalization is best viewed as representing (formulating, renaming knowledge) and abstracting (simplifying a given representation). The general process of...
Solving the Multiple-Instance Problem: A Lazy Learning Approach (2001)
As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being overall positive or...
Semantic Abstraction for Concept Representation and Learning (2001)
Jean-daniel Zucker, Place Jussieu
So far, abstraction has been mainly investigated in problem solving tasks. In this paper, we are interested in the role of abstraction in representing and learning concepts (i.e., intensional...
Representation Changes for Efficient Learning in Structural Domains (2001)
Jean-daniel Zucker, Jean-gabriel Ganascia, Laforia Ibp-cnrs, Laforia Ibp Cnrs
This paper presents an efficient approach to address the task of learning from large number of learning examples in structural domains. While in attribute-value representations only one mapping is...
DICE: a Discovery Environment integrating Inductive Bias (2001)
Jean-daniel Zucker, Vincent Corruble, Jrme Thomas, Geber Ramalho
: Most of Knowledge Discovery in Database (KDD) systems are integrating efficient Machine Learning techniques. In fact issues in Machine Learning and KDD are very close allowing for a natural...
Abstraction-Based Machine Learning Approach To Cartographic Generalisation (2001)
Sbastien Mustire, Jean-daniel Zucker, Lorenza Saitta, Tecnologie Avanzate
This article proposes a machine learning approach to overcome the knowledge acquisition bottleneck limiting the automation of cartographic generalisation. It first explains why this automation must...
Semantic Abstraction for Concept Representation and Learning (2001)
Lorenza Saitta, Jean-daniel Zucker, Place Jussieu
So far, abstraction has been mainly investigated in problem solving tasks. In this paper, we are interested in the role of abstraction in representing and learning concepts (i.e., intensional...
Collaborative Filtering Methods based on Fuzzy Preference Relations (2001)
Patrice Perny, Jean-daniel Zucker, Place Jussieu
This paper introduces a new approach for decision support. It is characterized by a collaborativedecision making process relying on the implicit sharing of preferences and experience between...
Solving Multiple-Instance Problem: A Lazy Learning Approach (2000)
Wang, Jun, Zucker, Jean-Daniel
As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being overall positive or...
Solving Multiple-Instance Problem: A Lazy Learning Approach (2000)
Wang, Jun, Zucker, Jean-Daniel
As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being overall positive or...
Solving Multiple-Instance Problem: A Lazy Learning Approach (2000)
Wang, Jun, Zucker, Jean-Daniel
As opposed to traditional supervised learning, multiple-instance learning concerns the problem of classifying a bag of instances, given bags that are labeled by a teacher as being overall positive or...
Learning Structurally Indeterminate Clauses (1998)
Jean-daniel Zucker, Jean-gabriel Ganascia, Place Jussieu
. This paper describes a new kind of language bias, S-structural indeterminate clauses, which takes into account the meaning of predicates that play a key role in the complexity of learning in...
Semantic Abstraction for (1998)
Lorenza Saitta, Jean-daniel Zucker
So far, abstraction has been mainly investigated in problem solving tasks. In this paper, we are interested in the role of abstraction in representing and learning concepts (i.e., intensional...
Selective Reformulation of Examples in Concept Learning (1996)
Jean-daniel Zucker, Jean-gabriel Ganascia
The fundamental tradeoff that is well known in Knowledge Representation and Reasoning affects Concept Learning from Examples too. Representation of learning examples using attribute-value has proved...
Integrating Machine Learning Techniques in a Guided Discovery Tutoring Environment: MEMOCAR (1996)
This chapter presents how Machine Learning Techniques can effectively contribute to improve the quality of interactions in Guided Discovery Tutoring Environments (GDTE) . We review several approaches...
Bredeche, Nicolas, Shi, Zhongzhi, Zucker, Jean-Daniel
This paper deals with the possible benefits of perceptual learning in artificial intelligence. On the one hand, perceptual learning is more and more studied in neurobiology and is now considered as...
A grounded theory of abstraction in artificial intelligence.
In artificial intelligence, abstraction is commonly used to account for the use of various levels of details in a given representation language or the ability to change from one level to another...
Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders
Mutch, David M., Temanni, M. Ramzi, Henegar, Corneliu, Combes, Florence, Pelloux, Véronique, Holst, Claus, ...
Henegar, Corneliu, Tordjman, Joan, Achard, Vincent, Lacasa, Danièle, Cremer, Isabelle, Guerre-Millo, Michèle, ...
Analysis of the transcriptomic signature of white adipose tissue in obese human subjects revealed increased interstitial fibrosis and an infiltration of inflammatory cells into the tissue.