|Title||Experiments with single-class support vector data descriptions as a tool for vocabulary grounding|
|Author(s)||Chauhan, Aneesh; Lopes, Luís Seabra|
|Source||In: Proceedings of the 7th International Workshop on Natural Language Processing and Cognitive Science, NLPCS 2010, in Conjunction with ICEIS 2010. - - p. 70 - 78.|
|Event||7th International Workshop on Natural Language Processing and Cognitive Science, NLPCS 2010, in Conjunction with ICEIS 2010, Funchal, 2010-06-08/2010-06-09|
|Publication type||Contribution in proceedings|
This paper explores support vectors as a tool for vocabulary acquisition in robots. The intention is to investigate the language grounding process at the single-word stage. A social language grounding scenario is designed, where a robotic agent is taught the names of the objects by a human instructor. The agent grounds the names of these objects by associating them with their respective sensor-based category descriptions. A system for grounding vocabulary should be incremental, adaptive and support gradual evolution. A novel learning model based on single-class support vector data descriptions (SVDD), which conforms to these requirements, is presented. For robustness and flexibility, a kernel based implementation of support vectors was realized. For this purpose, a sigmoid kernel using histogram pyramid matching has been developed. The support vectors are trained based on an original approach using genetic algorithms. The model is tested over a series of semi-automated experiments and the results are reported.