Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 561092
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
Publication year 2010
Abstract

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.

Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.