An Error-Correcting Output Code Framework for Lifelong Learning without a Teacher

Shen Shyang Ho, Mathew Marchiano, Scott Zockoll, Hieu Nguyen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    An intelligent system should learn new concepts continuously and autonomously. The system should recognize that a concept is new and learn the concept without any guidance. In this paper, a novel error correcting output code (ECOC)-based framework, motivated by the complementary learning systems (CLS) theory, is proposed to perform (i) rapid detection of a new concept and (ii) learning and storing of the new concept via reinforced encoding. Experimental results on six datasets show the feasibility and competitive performance of the proposed ECOC-based framework for life-long learning using four different base classifiers against two baseline approaches. Moreover, we demonstrate the performance of our proposed ECOC-based framework on a continual learning scenario without any label feedback using a realistic but stringent cumulative performance measure, which combines detection error and classification error.

    Original languageEnglish (US)
    Title of host publicationProceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020
    EditorsMiltos Alamaniotis, Shimei Pan
    PublisherIEEE Computer Society
    Pages249-254
    Number of pages6
    ISBN (Electronic)9781728192284
    DOIs
    StatePublished - Nov 2020
    Event32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020 - Virtual, Baltimore, United States
    Duration: Nov 9 2020Nov 11 2020

    Publication series

    NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
    Volume2020-November
    ISSN (Print)1082-3409

    Conference

    Conference32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020
    Country/TerritoryUnited States
    CityVirtual, Baltimore
    Period11/9/2011/11/20

    All Science Journal Classification (ASJC) codes

    • Software
    • Artificial Intelligence
    • Computer Science Applications

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