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- Céline Rouveirol, Véronique Ventos
- ILP
- 2000

- Michèle Sebag, Céline Rouveirol
- IJCAI
- 1997

Learning in first-order logic (FOL) languages suffers from a specific difficulty: both induction and classification are potentially exponential in the size of hypotheses. This difficulty is usually dealt wi th by l imit ing the size of hypotheses, via either syntactic restrictions or search strategies. This paper is concerned with polynomial induction and… (More)

- Céline Rouveirol
- Machine Learning
- 1994

Two representation changes are presented: the first one, called flattening, transforms a first-order logic program with function symbols into an equivalent logic program without function symbols; the second one, called saturation, completes an example description with relevant information with respect to both the example and available background knowledge.… (More)

- Nesserine Benchettara, Rushed Kanawati, Céline Rouveirol
- 2010 International Conference on Advances in…
- 2010

This work copes with the problem of link prediction in large-scale two-mode social networks. Two variations of the link prediction tasks are studied: predicting links in a bipartite graph and predicting links in a unimodal graph obtained by the projection of a bipartite graph over one of its node sets. For both tasks, we show in an empirical way, that… (More)

- Érick Alphonse, Céline Rouveirol
- ECAI
- 2000

A number of Inductive Logic Programming (ILP) systems have addressed the problem of learning First Order Logic (FOL) discriminant definitions by first reformulating the FOL learning problem into an attribute-value one and then applying efficient learning techniques dedicated to this simpler formalism. The complexity of such propositionalisation methods is… (More)

- Céline Rouveirol, Nicolas Stransky, +4 authors François Radvanyi
- Bioinformatics
- 2006

MOTIVATION
The identification of recurrent genomic alterations can provide insight into the initiation and progression of genetic diseases, such as cancer. Array-CGH can identify chromosomal regions that have been gained or lost, with a resolution of approximately 1 mb, for the cutting-edge techniques. The extraction of discrete profiles from raw array-CGH… (More)

- Philippe La Rosa, Eric Viara, +19 authors Emmanuel Barillot
- Bioinformatics
- 2006

MOTIVATION
Microarray-based CGH (Comparative Genomic Hybridization), transcriptome arrays and other large-scale genomic technologies are now routinely used to generate a vast amount of genomic profiles. Exploratory analysis of this data is crucial in helping to understand the data and to help form biological hypotheses. This step requires visualization of… (More)

- Nesserine Benchettara, Rushed Kanawati, Céline Rouveirol
- RecSys
- 2010

In this work we tackle the problem of link prediction in co-authoring network. We apply a topological dyadic supervised machine learning approach for that purpose. A co-authoring network is actually obtained by the projection of a two-mode graph (an authoring graph linking authors to publications they have signed) over the authors set. We show that link… (More)

- Céline Rouveirol
- IJCAI
- 1991

This paper presents an extension of the inverse resolution framework, which is a logical model of theory induction in first order logic. We first propose a definition of completeness for inductive procedures and exhibit some limitations of inverse resolution with respect to this definition. We then propose our model of inversion of logical entailment that… (More)

- Bruno Pradel, Savaneary Sean, +5 authors Frédéric Dufau-Joël
- KDD
- 2011

Collaborative filtering has been extensively studied in the context of ratings prediction. However, industrial recommender systems often aim at predicting a few items of immediate interest to the user, typically products that (s)he is likely to buy in the near future. In a collaborative filtering setting, the prediction may be based on the user's purchase… (More)