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ICML
2005
IEEE
14 years 5 months ago
Learning from labeled and unlabeled data on a directed graph
We propose a general framework for learning from labeled and unlabeled data on a directed graph in which the structure of the graph including the directionality of the edges is co...
Bernhard Schölkopf, Dengyong Zhou, Jiayuan Hu...
ACL
2007
13 years 6 months ago
A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as...
Feiyu Xu, Hans Uszkoreit, Hong Li
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 6 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
RECOMB
2000
Springer
13 years 8 months ago
Using Bayesian networks to analyze expression data
DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a "snapshot" of transcription levels within the c...
Nir Friedman, Michal Linial, Iftach Nachman, Dana ...
ICIP
2002
IEEE
14 years 6 months ago
Simultaneous mesh simplification and noise smoothing of range images
In this paper, we propose a novel algorithm to smooth and simplify simultaneously range images and also triangle meshes derived from those images. These data sets often suffer fro...
Yiyong Sun, Joon Ki Paik, Andreas Koschan, David L...