Experiments with Single-class Support Vector Data Descriptions as a Tool for Vocabulary Grounding

Aneesh Chauhan, L. Lopes
{"title":"Experiments with Single-class Support Vector Data Descriptions as a Tool for Vocabulary Grounding","authors":"Aneesh Chauhan, L. Lopes","doi":"10.5220/0003028000700078","DOIUrl":null,"url":null,"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.","PeriodicalId":378427,"journal":{"name":"International Workshop on Natural Language Processing and Cognitive Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Natural Language Processing and Cognitive Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0003028000700078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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.
单类支持向量数据描述作为词汇基础工具的实验
本文探讨了支持向量在机器人词汇习得中的应用。目的是研究在单字阶段的语言基础过程。设计了一个社会语言基础场景,其中机器人代理由人类讲师教授物体的名称。代理通过将这些对象与其各自的基于传感器的类别描述相关联来确定这些对象的名称。词汇基础系统应该是渐进的、适应性强的、支持渐进进化的。提出了一种新的基于单类支持向量数据描述(SVDD)的学习模型。为了保证鲁棒性和灵活性,实现了基于核的支持向量实现。为此,开发了一种使用直方图金字塔匹配的s型核。基于遗传算法的原始方法训练支持向量。通过一系列半自动化实验对模型进行了测试,并报告了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信