20ème édition de la conférence Extraction et Gestion des Connaissances (EGC)

Accueil - enregistrement ateliers & conférence

Accueil au desk d'entrée du 4, Boulevard Brand Withlock.

é-EGC : Yves Kodratoff - “Passer à ou revenir à « Machine Reasoning » ?”

Partie 1 : Quelques ‘souvenirs’ de l’IA des années 70 L’IA est-elle une branche de l’informatique ou bien la science des explications ? « Alice » (1976) de Jean-Louis Laurière (1945-2005).
Salle 201 Partie 2 : L’interaction homme-machine pour implémenter un « Extra-Strong Learning »: prendre en compte les échecs et les succès (innovations) des programmeurs PROLOG. Approche de Stephen Muggleton & all. Partie 3 : Vers une théorie de la créativité scientifique : systèmes complexes symbiotiques (symbiose ‘orientée vers un but’), « pulsatifs » (savoir prouver des théorèmes ‘existentiels’ de la forme $ System " Problem solves(System,Problem)), travailler avec des systèmes « presque complets » (gérer les spécifications incomplètes).

Atelier DAHLIA

Salle 231

Atelier APTA

Salle 221

Atelier DL for NLP

Salle 431

Atelier VIF

Salle 232

Atelier HIA

Salle 222

Pause café

Espace Roger Demain- Cafétéria

é-EGC : Michel Verleysen - “Dimensionality Reduction and Manifold Learning for High-Dimensional Data Analysis ”

High-dimensional data are ubiquitous in many branches of science: sociology, psychometrics, medicine, and many others. Modern data science faces huge challenges in extracting useful information from these data. Indeed high-dimensional data have statistical properties that make them ill-adapted to conventional data analysis tools. In addition the choice among the wide range of modern machine learning tools is difficult because it should be guided by the (unknown) structure and properties of the data. Finally, understanding the results of data analysis may be even more important than the performances in many applications because of the need to convince users and experts. These reasons makes dimensionality reduction, including (but not restricted to) visualization, of high-dimensional data an essential step in the data analysis process. Dimensionality reduction (DR) aims at providing faithful low-dimensional (LD) representations of high-dimensional (HD) data. Feature selection is a branch of DR that selects a low number of features among the original HD ones; keeping the original features helps user interpretability. Other DR methods provides more flexibility by building new features as nonlinear combinations of the original ones, at the cost of a lower interpretability. This talk will cover advances in machine learning based dimensionality reduction. The curse of dimensionality and its influence on algorithms will be detailed as a motivation for DR methods. Next, the tutorial will cover information-theoretic criteria for feature selection, in particular mutual information used for multivariate selection. Finally the talk will cover advances in nonlinear dimensionality reduction related to manifold learning: after a brief historical perspective, it will present modern DR methods relying on distance, neighborhood or similarity preservation, and using either spectral methods or nonlinear optimization tools. It will cover important issues such as scalability to big data, user interaction for dynamical exploration, reproducibility, stability, and performance evaluation.
Salle 201

Atelier DAHLIA

Salle 231

Atelier APTA

Salle 221

Atelier DL for NLP

Salle 431

Atelier VIF

Salle 232

Atelier HIA

Salle 222

Déjeuner

Espace Roger Demain- Cafétéria

é-EGC : Arnaud Martin - “Classifier fusion and imperfect data management

Classifier fusion methods are particular cases of information fusion. Voting methods allow the reliability of classifiers to be integrated but are not able to represent the imperfections of the output of classifiers. Probability theory makes it possible to model uncertainty but not the imprecision of classifiers. The theory of belief functions is widely used in information fusion because it models the uncertainty and imprecision of classifiers and their reliability. When combining imperfect experts' opinions the conflict is unavoidable. In the theory of belief functions one of the major problem is the global conflict repartition enlightened by the famous Zadeh’s example. As a consequence, a plethora of alternative combination rules to Dempster’s one were born, in particular proposing alternative repartitions of conflict . The global conflict is traditionally defined by the weight assigned to the empty set after a conjunctive rule. However, this quantity fails to adequately represent the disagreement between experts in particular when noticing that the conflict between identical belief functions is not null due to the non-idempotence of the majority of the rules . This lecture presents information fusion principles with vote methods, probabilistic methods and the theory of belief function. Some definitions of conflict measures and how to manage the conflict in the framework of the theory of belief functions are presented.
Salle 201

Atelier DAHLIA

Salle 231

Atelier TextMine

Salle 222

Atelier GAST

Salle 221

Atelier DL for NLP

Salle 431

Atelier AWD

Salle 232

é-EGC : Clôture de l'école

Clôture de l'école.
Salle 201

Pause café

Espace Roger Demain- Cafétéria

Atelier DAHLIA

Salle 231

Atelier TextMine

Salle 222

Atelier GAST

Salle 221

Atelier AWD

Salle 232

Conférence > Programme général