@inproceedings{c4c2c84e9ee24771a0f4cae42fe49d0d,
title = "Transductive learning: Learning Iris data with two labeled data",
abstract = "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.",
author = "Li, {Chun Hung} and Yuen, {Pong Chi}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; International Conference on Artificial Neural Networks, ICANN 2001 ; Conference date: 21-08-2001 Through 25-08-2001",
year = "2001",
doi = "10.1007/3-540-44668-0_33",
language = "English",
isbn = "3540424865",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "231--236",
editor = "Kurt Hornik and Georg Dorffner and Horst Bischof",
booktitle = "Artificial Neural Networks - ICANN 2001 - International Conference, Proceedings",
address = "Germany",
}