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» Semi-supervised Feature Selection via Spectral Analysis
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ICML
2003
IEEE
14 years 11 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
ACL
2009
13 years 8 months ago
A Graph-based Semi-Supervised Learning for Question-Answering
We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Using textual entailment analysis, we obtain entailment sco...
Asli Çelikyilmaz, Marcus Thint, Zhiheng Hua...
SDM
2007
SIAM
137views Data Mining» more  SDM 2007»
14 years 8 days ago
Semi-supervised Feature Selection via Spectral Analysis
Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called “small labeled-sample problem” in which labeled data is...
Zheng Zhao, Huan Liu
CIVR
2008
Springer
279views Image Analysis» more  CIVR 2008»
14 years 19 days ago
Semi-supervised learning of object categories from paired local features
This paper presents a semi-supervised learning (SSL) approach to find similarities of images using statistics of local matches. SSL algorithms are well known for leveraging a larg...
Wen Wu, Jie Yang
JRTIP
2006
114views more  JRTIP 2006»
13 years 10 months ago
Sensor band selection for multispectral imaging via average normalized information
The information-rich scene descriptors created by multispectral sensors can act as a bottleneck in further analysis. Many of the spectral band selection methods treat the two under...
Hongzhi Wang, Elli Angelopoulou