TY - GEN
T1 - Learning classifiers on a partially labeled data manifold
AU - Qiuhua, Liu
AU - Xuejun, Liao
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2007/8/6
Y1 - 2007/8/6
N2 - We present an algorithm for learning parametric classifiers on a partially labeled data manifold, based on a graph representation of the manifold. The unlabeled data are utilized by basing classifier learning on neighborhoods, formed via Markov random, walks. The proposed algorithm, yields superior performance on three benchmark data sets and the margin of improvements over existing semi-supervised algorithms is significant. © 2007 IEEE.
AB - We present an algorithm for learning parametric classifiers on a partially labeled data manifold, based on a graph representation of the manifold. The unlabeled data are utilized by basing classifier learning on neighborhoods, formed via Markov random, walks. The proposed algorithm, yields superior performance on three benchmark data sets and the margin of improvements over existing semi-supervised algorithms is significant. © 2007 IEEE.
UR - https://ieeexplore.ieee.org/document/4217485/
UR - http://www.scopus.com/inward/record.url?scp=34547547371&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2007.366312
DO - 10.1109/ICASSP.2007.366312
M3 - Conference contribution
SN - 1424407281
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -