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» Learning from Highly Structured Data by Decomposition
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ICCV
2007
IEEE
15 years 12 months ago
Lung Nodule Growth Analysis from 3D CT Data with a Coupled Segmentation and Registration Framework
In this paper we propose a new framework to simultaneously segment and register lung and tumor in serial CT data. Our method assumes nonrigid transformation on lung deformation an...
Yuanjie Zheng, Karl Steiner, Thomas Bauer, Jingyi ...
CIKM
2009
Springer
15 years 4 months ago
Semi-supervised learning of semantic classes for query understanding: from the web and for the web
Understanding intents from search queries can improve a user’s search experience and boost a site’s advertising profits. Query tagging via statistical sequential labeling mode...
Ye-Yi Wang, Raphael Hoffmann, Xiao Li, Jakub Szyma...
SCALESPACE
2007
Springer
15 years 4 months ago
Non-negative Sparse Modeling of Textures
This paper presents a statistical model for textures that uses a non-negative decomposition on a set of local atoms learned from an exemplar. This model is described by the varianc...
Gabriel Peyré
GCB
2006
Springer
136views Biometrics» more  GCB 2006»
15 years 1 months ago
Ab Initio Prediction of Molecular Fragments from Tandem Mass Spectrometry Data
: Mass spectrometry is one of the key enabling measurement technologies for systems biology, due to its ability to quantify molecules in small concentrations. Tandem mass spectrome...
Markus Heinonen, Ari Rantanen, Taneli Mielikä...
ECAI
2006
Springer
15 years 1 months ago
Least Squares SVM for Least Squares TD Learning
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
Tobias Jung, Daniel Polani