Interactive and Incremental Learning via a Mixture of Supervised and Unsupervised Learning Strategies

Qiong Liu*, Stephen Levinson, Ying Wu, Thomas Huang

*Corresponding author for this work

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

4 Scopus citations

Abstract

Machine learning paradigms are generally separated into supervised learning and unsupervised learning. Both of these paradigms have their own advantages in practice. But existing algorithms of these two paradigms also expose some hard problems in many different applications. In this paper, we first analyze the general problems of these two paradigms, and some successful techniques for boosting their performance. Then we propose a novel algorithm that can overcome some of existing problems through a mixture of these two paradigms. The algorithm is tested with a robot language-learning task. Equipped with this algorithm, our robot is able to acquire short audio information online, and gradually understand the audio input through human's intensive teaching.

Original languageEnglish (US)
Title of host publicationProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 1
EditorsP.P. Wang, P.P. Wang
Pages555-558
Number of pages4
Edition1
StatePublished - Dec 1 2000
EventProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000 - Atlantic City, NJ, United States
Duration: Feb 27 2000Mar 3 2000

Publication series

NameProceedings of the Joint Conference on Information Sciences
Number1
Volume5

Other

OtherProceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000
CountryUnited States
CityAtlantic City, NJ
Period2/27/003/3/00

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Liu, Q., Levinson, S., Wu, Y., & Huang, T. (2000). Interactive and Incremental Learning via a Mixture of Supervised and Unsupervised Learning Strategies. In P. P. Wang, & P. P. Wang (Eds.), Proceedings of the Fifth Joint Conference on Information Sciences, JCIS 2000, Volume 1 (1 ed., pp. 555-558). (Proceedings of the Joint Conference on Information Sciences; Vol. 5, No. 1).