On-demand data numerosity reduction for learning artifacts

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

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

In domains in which single agent learning is a more natural metaphor for an artifact-embedded agent, Exemplar-Based Learning (EBL) requires significantly large sets of training examples for it to be applicable. Obviously large sets of training examples contradict resource capabilities of artifacts. To make EBL a possibility for these artifacts, sets of training examples must be reduced in size in a way that does not compromise learning performance in order to relieve artifacts' resources (e.g. memory). In this paper, we investigate training sets requirements for artifacts learning and propose a ranking-based Stratified Ordered Selection (SOS) method to scale them down. Contrary to reduction approaches in mainstream learning, this method has been designed with resource constraint nature of artifacts in mind. Artifacts shall use an intermediary which implements SOS to, dynamically and on-demand, retrieve training subsets based on their resource capacities (e.g. memory, CPU). SOS uses a new Level Order (LO) ranking scheme which has been designed to broaden representation of classes of examples, to quicken data retrieval, and to allow for retrieval of subsets of varying sizes while ensuring same or near same learning performance. We present how SOS evaluates on various well known machine learning datasets and how it compares to some of the best performing data reduction approaches.

本文言語English
ホスト出版物のタイトルProceedings - 26th IEEE International Conference on Advanced Information Networking and Applications, AINA 2012
ページ152-159
ページ数8
DOI
出版ステータスPublished - 2012 5 14
イベント26th IEEE International Conference on Advanced Information Networking and Applications, AINA 2012 - Fukuoka, Japan
継続期間: 2012 3 262012 3 29

出版物シリーズ

名前Proceedings - International Conference on Advanced Information Networking and Applications, AINA
ISSN(印刷版)1550-445X

Other

Other26th IEEE International Conference on Advanced Information Networking and Applications, AINA 2012
国/地域Japan
CityFukuoka
Period12/3/2612/3/29

ASJC Scopus subject areas

  • 工学(全般)

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