A life-long learning vector quantization approach for interactive learning of multiple categories.
We present a new method capable of learning multiple categories in an interactive and life-long learning fashion to approach the "stability-plasticity dilemma". The problem of incremental learning of multiple categories is still largely unsolved. This is especially true for the domain of cognitive robotics, requiring real-time and interactive learning. To achieve the life-long learning ability for a cognitive system, we propose a new learning vector quantization approach combined with a category-specific feature selection method to allow several metrical "views" on the representation space of each individual vector quantization node. These category-specific features are incrementally collected during the learning process, so that a balance between the correction of wrong representations and the stability of acquired knowledge is achieved. We demonstrate our approach for a difficult visual categorization task, where the learning is applied for several complex-shaped objects rotated in depth.
Honda Research Institute Europe GmbH, Carl-Legien-Str. 30, 63073 Offenbach am Main, Germany. Stephan.Kirstein@honda-ri.de
Stephan
S
Kirstein
Heiko
H
Wersing
Horst-Michael
HM
Gross
Edgar
E
Körner
D007858:Learning; D008136:Longevity; D012371:Robotics / Q000592:standards / Q000639:trends