Dedicated Memory Models for Continual Learning in the Presence of Concept Drift

Losing V, Hammer B, Wersing H. Dedicated Memory Models for Continual Learning in the Presence of Concept Drift. Presented at the Continual Learning Workshop of the Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Barcelona

Choosing the Best Algorithm for an Incremental On-line Learning Task

Losing V, Hammer B, Wersing H. Choosing the Best Algorithm for an Incremental On-line Learning Task. Presented at the European Symposium on Artificial Neural Networks, Brügge.Recently, incremental and on-line learning gained more attention especially in the context of big data and learning from data streams, conflicting with the traditional assumption of complete data availability. Even though a variety of different methods are available, it often remains unclear which of them is suitable for a specific task and how they perform in comparison to each other. We analyze the key properties of seven incremental methods representing different algorithm classes. Our extensive evaluation on data sets with different characteristics gives an overview of the performance with respect to accuracy as well as model complexity, facilitating the choice of the best method for a given application

Incremental on-line learning: A review and comparison of state of the art algorithms

Losing V, Hammer B, Wersing H. Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing. 2018;275:1261-1274

Personalized Maneuver Prediction at Intersections

Losing V, Hammer B, Wersing H. Personalized Maneuver Prediction at Intersections. Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama

Personalized Maneuver Prediction at Intersections

Losing V, Hammer B, Wersing H. Personalized Maneuver Prediction at Intersections. Presented at the IEEE Intelligent Transportation Systems Conference 2017, Yokohama

Adaptive scene dependent filters for segmentation and online learning of visual objects

Steil JJ, Götting M, Wersing H, Körner E, Ritter H. Adaptive scene dependent filters for segmentation and online learning of visual objects. Neurocomputing. 2007;70(7-9):1235-1246

Interactive Online Learning for Obstacle Classification on a Mobile Robot

Losing V, Hammer B, Wersing H. Interactive Online Learning for Obstacle Classification on a Mobile Robot. Presented at the International Joint Conference on Neural Networks, Killarney, Ireland.We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot. The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learning by adaptive insertions of representative vectors. We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy optimizing a cost-function based on a subset of samples. We demonstrate this model within a real-time application for a mobile robot scenario, where we perform interactive real-time learning of visual categories

Mitigating Concept Drift via Rejection

Göpfert JP, Hammer B, Wersing H. Mitigating Concept Drift via Rejection. In: Kurkova V, Manolopoulos Y, Hammer B, Iliadis L, Maglogiannis I, eds. Artificial Neural Networks and Machine Learning – ICANN 2018. Proceedings, Part I. Lecture Notes in Computer Science. Vol 11139. Cham: Springer; 2018