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» Learning from Highly Structured Data by Decomposition
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CVPR
2009
IEEE
16 years 5 months ago
Unsupervised Learning of Hierarchical Spatial Structures In Images
The visual world demonstrates organized spatial patterns, among objects or regions in a scene, object-parts in an object, and low-level features in object-parts. These classes o...
Devi Parikh (Carnegie Mellon University), C. Lawre...
JSA
2007
84views more  JSA 2007»
14 years 9 months ago
Optimizing data structures at the modeling level in embedded multimedia
Traditional design techniques for embedded systems apply transformations on the source code to optimize hardwarerelated cost factors. Unfortunately, such transformations cannot ad...
Marijn Temmerman, Edgar G. Daylight, Francky Catth...
MICCAI
2009
Springer
15 years 4 months ago
On the Manifold Structure of the Space of Brain Images
This paper investigates an approach to model the space of brain images through a low-dimensional manifold. A data driven method to learn a manifold from a collections of brain imag...
Samuel Gerber, Tolga Tasdizen, Sarang C. Joshi, Ro...
AAAI
2008
15 years 6 days ago
Text Categorization with Knowledge Transfer from Heterogeneous Data Sources
Multi-category classification of short dialogues is a common task performed by humans. When assigning a question to an expert, a customer service operator tries to classify the cu...
Rakesh Gupta, Lev-Arie Ratinov
BMCBI
2006
134views more  BMCBI 2006»
14 years 10 months ago
Application of machine learning in SNP discovery
Background: Single nucleotide polymorphisms (SNP) constitute more than 90% of the genetic variation, and hence can account for most trait differences among individuals in a given ...
Lakshmi K. Matukumalli, John J. Grefenstette, Davi...