Description
Date depot: 1 janvier 1900
Titre: Machine Learning Methods to Anomaly Detection
Directeur de thèse:
Stephan CLEMENCON (LTCI (EDMH))
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
Learning how to rank multivariate unlabelled observations depending on their degree of abnormality/novelty is a crucial problem in a wide range of applications. In practice, it generally consists in building a real valued 'scoring' function on the feature space so as to quantify to which extent observations should be considered as abnormal. In the 1-d situation, measurements are generally considered as ”abnormal” when they are remote from central measures such as the mean or the median. Extensions to the multivariate setting are far from straightforward and it is precisely the main purpose of this thesis to deal with this problem.
Doctorant.e: Goix Nicolas