TY  - BOOK
AB  - 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.
DA  - 2015
DO  - 10.1109/IJCNN.2015.7280610
KW  - online learning
KW  - outdoor object classification
KW  - Learning Vector Quantization
LA  - eng
PY  - 2015
TI  - Interactive Online Learning for Obstacle Classification on a Mobile Robot
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-27760218
Y2  - 2024-11-25T03:27:55
ER  -