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» Improving Gradient Estimation by Incorporating Sensor Data
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PAMI
2008
161views more  PAMI 2008»
13 years 7 months ago
Multilayered 3D LiDAR Image Construction Using Spatial Models in a Bayesian Framework
Standard 3D imaging systems process only a single return at each pixel from an assumed single opaque surface. However, there are situations when the laser return consists of multip...
Sergio Hernandez-Marin, Andrew M. Wallace, Gavin J...
ESANN
2006
13 years 9 months ago
Random Forests Feature Selection with K-PLS: Detecting Ischemia from Magnetocardiograms
Random Forests were introduced by Breiman for feature (variable) selection and improved predictions for decision tree models. The resulting model is often superior to AdaBoost and ...
Long Han, Mark J. Embrechts, Boleslaw K. Szymanski...
NIPS
1998
13 years 9 months ago
Semi-Supervised Support Vector Machines
We introduce a semi-supervised support vector machine (S3 VM) method. Given a training set of labeled data and a working set of unlabeled data, S3 VM constructs a support vector m...
Kristin P. Bennett, Ayhan Demiriz
ACL
2006
13 years 9 months ago
Discriminative Word Alignment with Conditional Random Fields
In this paper we present a novel approach for inducing word alignments from sentence aligned data. We use a Conditional Random Field (CRF), a discriminative model, which is estima...
Phil Blunsom, Trevor Cohn
BMCBI
2008
153views more  BMCBI 2008»
13 years 7 months ago
AWclust: point-and-click software for non-parametric population structure analysis
Background: Population structure analysis is important to genetic association studies and evolutionary investigations. Parametric approaches, e.g. STRUCTURE and L-POP, usually ass...
Xiaoyi Gao, Joshua D. Starmer