Laboratoire de l'Accélérateur Linéaire, U. Paris-Sud
Abstract
This thesis aims at applying the scientific method to machine learning along two lines of
research.
The first one builds on recent work applying modern experimental design for algorithm
selection and hyperparameter tuning (Brendel and Schoenauer (2011), Lacoste et al. (2012), Bardenet et al. (2013), Misir and Sebag 2013). We will explore the interaction between methods (and hyperparameters) and data sets to find out whether and to what extent experience can be
generalized across data sets. The output of this project is a toolbox for practitioners and a stockpile of knowledge on what algorithm works on what (kind of) data sets. T
The second one asks the question of why certain methods work on certain data sets. There are several principles, that explain the success of methods and guide algorithmic development; our
goal is to verify these principles and to discover new ones based on an empirical approach.
Context
Objectives
Work program
Extra information
Prerequisite
Détails
Expected funding
Institutional funding
Status of funding
Confirmed
Candidates
Utilisateur
michele-martine.sebag
Créé
Jeudi 12 juin 2014 15:49:04 CEST
dernière modif.
Jeudi 12 juin 2014 15:49:04 CEST
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Connexion
Ecole Doctorale Informatique Paris-Sud
Directrice
Nicole Bidoit Assistante
Stéphanie Druetta Conseiller aux thèses
Dominique Gouyou-Beauchamps
ED 427 - Université Paris-Sud
UFR Sciences Orsay
Bat 650 - aile nord - 417
Tel : 01 69 15 63 19
Fax : 01 69 15 63 87
courriel: ed-info à lri.fr