On-demand numerosity reduction for object learning

Khamisi Kalegele, Johan Sveholm, Hideyuki Takahashi, Kazuto Sasai, Gen Kitagata, Tetsuo Kinoshita

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In Internet of Things, softwares shall enable their host objects (everyday-objects) to monitor other objects, take actions, and notify humans while using some form of reasoning. The ever changing nature of real life environment necessitates the need for these objects to be able to generalize various inputs inductively in order to play their roles more effectively. These objects shall learn from stored training examples using some generalization algorithm. In this paper, we investigate training sets requirements for object learning and propose a Stratified Ordered Selection (SOS) method as a means to scale down training sets. SOS uses a new instance ranking scheme called LO ranking. Everyday-objects use SOS to select training subsets based on their capacity (e.g. memory, CPU). LO ranking has been designed to broaden class representation, achieve significant reduction while offering same or near same analytical results and to facilitate faster on-demand subset selection and retrieval for resource constrained objects. We show how SOS outperforms other methods using well known machine learning datasets.

本文言語English
ホスト出版物のタイトルProceedings of the Workshop on Internet of Things and Service Platforms, IoTSP'11
DOI
出版ステータスPublished - 2011 12 1
イベントWorkshop on Internet of Things and Service Platforms, IoTSP'11 - Tokyo, Japan
継続期間: 2011 12 62011 12 9

出版物シリーズ

名前Proceedings of the Workshop on Internet of Things and Service Platforms, IoTSP'11

Other

OtherWorkshop on Internet of Things and Service Platforms, IoTSP'11
国/地域Japan
CityTokyo
Period11/12/611/12/9

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信

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