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BMCBI
2008
138views more  BMCBI 2008»
14 years 9 months ago
Combining transcriptional datasets using the generalized singular value decomposition
Background: Both microarrays and quantitative real-time PCR are convenient tools for studying the transcriptional levels of genes. The former is preferable for large scale studies...
Andreas W. Schreiber, Neil J. Shirley, Rachel A. B...
VIS
2008
IEEE
214views Visualization» more  VIS 2008»
15 years 11 months ago
Estimating Crossing Fibers: A Tensor Decomposition Approach
Diffusion weighted magnetic resonance imaging is a unique tool for non-invasive investigation of major nerve fiber tracts. Since the popular diffusion tensor (DT-MRI) model is limi...
Thomas Schultz, Hans-Peter Seidel
GBRPR
2009
Springer
15 years 4 months ago
Texture Segmentation by Contractive Decomposition and Planar Grouping
Abstract. Image segmentation has long been an important problem in the computer vision community. In our recent work we have addressed the problem of texture segmentation, where we...
Anders Bjorholm Dahl, Peter Bogunovich, Ali Shokou...
ML
2008
ACM
14 years 9 months ago
A bias/variance decomposition for models using collective inference
Bias/variance analysis is a useful tool for investigating the performance of machine learning algorithms. Conventional analysis decomposes loss into errors due to aspects of the le...
Jennifer Neville, David Jensen
ICCAD
2007
IEEE
96views Hardware» more  ICCAD 2007»
15 years 6 months ago
Monte-Carlo driven stochastic optimization framework for handling fabrication variability
Increasing effects of fabrication variability have inspired a growing interest in statistical techniques for design optimization. In this work, we propose a Monte-Carlo driven sto...
Vishal Khandelwal, Ankur Srivastava