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ICML
2003
IEEE
16 years 7 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
NIPS
1998
15 years 8 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
IJCNN
2006
IEEE
16 years 22 days ago
P-SVM Variable Selection for Discovering Dependencies Between Genetic and Brain Imaging Data
— The joint analysis of genetic and brain imaging data is the key to understand the genetic underpinnings of brain dysfunctions in several psychiatric diseases known to have a st...
Johannes Mohr, Imke Puis, Jana Wrase, Sepp Hochrei...
OTM
2010
Springer
15 years 4 months ago
Towards Evaluating GRASIM for Ontology-Based Data Matching
The GRASIM (Graph-Aided Similarity calculation) algorithm is designed to solve the problem of ontology-based data matching. We subdivide the matching problem into the ones of restr...
Yan Tang
IPPS
2008
IEEE
16 years 1 months ago
Data throttling for data-intensive workflows
— Existing workflow systems attempt to achieve high performance by intelligently scheduling tasks on resources, sometimes even attempting to move the largest data files on the hi...
Sang-Min Park, Marty Humphrey