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» Learning from Highly Structured Data by Decomposition
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BMCBI
2007
111views more  BMCBI 2007»
15 years 4 months ago
Modular co-evolution of metabolic networks
Background: The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known f...
Jing Zhao, Guohui Ding, Lin Tao, Hong Yu, Zhong-Ha...
153
Voted
ICDE
2010
IEEE
290views Database» more  ICDE 2010»
15 years 8 months ago
The Model-Summary Problem and a Solution for Trees
Modern science is collecting massive amounts of data from sensors, instruments, and through computer simulation. It is widely believed that analysis of this data will hold the key ...
Biswanath Panda, Mirek Riedewald, Daniel Fink
KDD
2004
ACM
209views Data Mining» more  KDD 2004»
16 years 4 months ago
Tracking dynamics of topic trends using a finite mixture model
In a wide range of business areas dealing with text data streams, including CRM, knowledge management, and Web monitoring services, it is an important issue to discover topic tren...
Satoshi Morinaga, Kenji Yamanishi
109
Voted
LREC
2010
131views Education» more  LREC 2010»
15 years 5 months ago
A Semi-supervised Type-based Classification of Adjectives: Distinguishing Properties and Relations
We present a semi-supervised machine-learning approach for the classification of adjectives into property- vs. relationdenoting adjectives, a distinction that is highly relevant f...
Matthias Hartung, Anette Frank
133
Voted
CVPR
2010
IEEE
16 years 4 days ago
Automatic Discovery of Meaningful Object Parts with Latent CRFs
Object recognition is challenging due to high intra-class variability caused, e.g., by articulation, viewpoint changes, and partial occlusion. Successful methods need to strike a...
Paul Schnitzspan, Stefan Roth, Bernt Schiele