Incorporating expert feedback into anomaly detection using support vector machines
Title : Incorporating expert feedback into anomaly detection using support vector machine
Asbtract : Anomaly detection consists of detecting elements of a database that are different from the majority of normal data. The majority of anomaly detection algorithms is adapted to unlabeled datasets. However, in some applications, labels associated with a subset of the database (coming for instance from expert feedback) are available, providing useful information to design the anomaly detector. This presentation introduces a semi-supervised anomaly detector based on support vector machines, which takes the best of existing supervised and unsupervised Support Vector Machines (SVM) algorithms.
The first part is dedicated to the introduction of supervised SVM and their application to unsupervised anomaly detection, then we will explain how anomaly dectection can be performed using SVM with partially labelled datasets and how this can be applied to anomaly detection with expert feedback, and we will finish by presenting current studies and potential future works
Contact : http://perso.tesa.prd.fr/jlesouple/index.html
Séminaire en ligne : https://lpensl.my.webex.com/
Room : Webconf3