An Error-Correcting Output Code Framework for Lifelong Learning without a Teacher
Shen-Shyang Ho, Mathew Marchiano, Scott Zockoll, Hieu Duc Nguyen · 2020
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.