54 citations · 227 across the 36 of their papers we have counts for
8 papers · 1 filter
Boltzmann Tuning of Generative Models
Victor Berger, Michele Sebag
The paper focuses on the a posteriori tuning of a generative model in order to favor the generation of good instances in the sense of some external differentiable criterion. The pr…
Scaling Analysis of Affinity Propagation
Cyril Furtlehner, Michele Sebag, Xiangliang Zhang
We analyze and exploit some scaling properties of the Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007). First we observe that a divide and conquer s…
Evolving Symbolic Controllers
Nicolas Godzik, Marc Schoenauer, Michèle Sebag
The idea of symbolic controllers tries to bridge the gap between the top-down manual design of the controller architecture, as advocated in Brooks' subsumption architecture, and th…
Ensemble Learning for Free with Evolutionary Algorithms ?
Christian Gagné, Michèle Sebag, Marc Schoenauer +1
Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final re…
Functional Brain Imaging with Multi-Objective Multi-Modal Evolutionary Optimization
Vojtech Krmicek, Michèle Sebag
Functional brain imaging is a source of spatio-temporal data mining problems. A new framework hybridizing multi-objective and multi-modal optimization is proposed to formalize thes…
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Christian Gagné, Marc Schoenauer, Michèle Sebag +1
Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed optimization p…