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» An Introduction to Learning Structured Information
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CHI
2009
ACM
16 years 4 months ago
Two studies of opportunistic programming: interleaving web foraging, learning, and writing code
This paper investigates the role of online resources in problem solving. We look specifically at how programmers--an exemplar form of knowledge workers--opportunistically interlea...
Joel Brandt, Philip J. Guo, Joel Lewenstein, Mira ...
KDD
2008
ACM
161views Data Mining» more  KDD 2008»
16 years 4 months ago
Spectral domain-transfer learning
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...
Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, ...
ICCV
2007
IEEE
15 years 10 months ago
Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features
Multi-chamber heart segmentation is a prerequisite for global quantification of the cardiac function. The complexity of cardiac anatomy, poor contrast, noise or motion artifacts ...
Yefeng Zheng, Adrian Barbu, Bogdan Georgescu, Mich...
CIKM
2008
Springer
15 years 6 months ago
A latent variable model for query expansion using the hidden markov model
We propose a novel probabilistic method based on the Hidden Markov Model (HMM) to learn the structure of a Latent Variable Model (LVM) for query language modeling. In the proposed...
Qiang Huang, Dawei Song
IPMI
2005
Springer
16 years 4 months ago
Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images
Having accurate left ventricle (LV) segmentations across a cardiac cycle provides useful quantitative (e.g. ejection fraction) and qualitative information for diagnosis of certain ...
Alan S. Willsky, Godtfred Holmvang, Müjdat &C...