Global/local hybrid learning of mixture-of-experts from labeled and unlabeled data

Jong Won Yoon, Sung Bae Cho

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

3 Citations (Scopus)


The mixture-of-experts (ME) models can be useful to solve complicated classification problems in real world. However, in order to train the ME model with not only labeled data but also unlabeled data which are easier to come, a new learning algorithm that considers characteristics of the ME model is required. We proposed global-local co-training (GLCT), the hybrid training method of the ME model training method for supervised learning (SL) and the co-training, which trains the ME model in semi-supervised learning (SSL) manner. GLCT uses a global model and a local model together since using the local model only shows low accuracy due to lack of labeled training data. The models enlarge the labeled data set from the unlabeled one and are trained from it by supplementing each other. To evaluate the method, we performed experiments using benchmark data sets from UCI machine learning repository. As the result, GLCT confirmed the feasibility of itself. Moreover, a comparison experiments to show the excellences of GLCT showed better performance than the other alternative method.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligent Systems - 6th International Conference, HAIS 2011, Proceedings
PublisherSpringer Verlag
Number of pages8
EditionPART 1
ISBN (Print)9783642212185
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6678 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Funding Information:
Acknowledgement. This research was supported by the Converging Research Center Program through the Converging Research Headquarter for Human, Cognition and Environment funded by the Ministry of Education, Science and Technology (2010K001173).

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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