Transductive learning: Learning Iris data with two labeled data

Chun Hung Li, Pong Chi YUEN

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

3 Citations (Scopus)


This paper presents two graph-based algorithms for solving the transductive learning problem. Stochastic contraction algorithms with similarity based sampling and normalized similarity based sampling are introduced. The transductive learning on a classical problem of plant iris classification achieves an accuracy of 96% with only 2 labeled data while previous research has often used 100 training samples. The quality of the algorithm is also empirically evaluated on a synthetic clustering problem and on the iris plant data.

Original languageEnglish
Title of host publicationArtificial Neural Networks - ICANN 2001 - International Conference, Proceedings
EditorsKurt Hornik, Georg Dorffner, Horst Bischof
PublisherSpringer Verlag
Number of pages6
ISBN (Print)3540424865, 9783540446682
Publication statusPublished - 2001
EventInternational Conference on Artificial Neural Networks, ICANN 2001 - Vienna, Austria
Duration: 21 Aug 200125 Aug 2001

Publication series

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


ConferenceInternational Conference on Artificial Neural Networks, ICANN 2001

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Transductive learning: Learning Iris data with two labeled data'. Together they form a unique fingerprint.

Cite this