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PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
14 years 1 months ago
The Feature Importance Ranking Measure
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access...
Alexander Zien, Nicole Krämer, Sören Son...
SCFBM
2008
138views more  SCFBM 2008»
13 years 6 months ago
Epigrass: a tool to study disease spread in complex networks
Background: The construction of complex spatial simulation models such as those used in network epidemiology, is a daunting task due to the large amount of data involved in their ...
Flávio C. Coelho, Oswaldo G. Cruz, Cl&aacut...
JMLR
2010
192views more  JMLR 2010»
13 years 2 months ago
Inducing Tree-Substitution Grammars
Inducing a grammar from text has proven to be a notoriously challenging learning task despite decades of research. The primary reason for its difficulty is that in order to induce...
Trevor Cohn, Phil Blunsom, Sharon Goldwater
ICML
2003
IEEE
14 years 8 months ago
Learning on the Test Data: Leveraging Unseen Features
This paper addresses the problem of classification in situations where the data distribution is not homogeneous: Data instances might come from different locations or times, and t...
Benjamin Taskar, Ming Fai Wong, Daphne Koller
ICCV
2011
IEEE
12 years 7 months ago
Decision Tree Fields
This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fiel...
Sebastian Nowozin, Carsten Rother, Shai Bagon, Ban...