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» Learning Classifiers from Semantically Heterogeneous Data
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CVPR
2008
IEEE
13 years 9 months ago
Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection
A large variety of image features has been invented for detection of objects of a known class. We propose a framework to optimize the discrimination-efficiency tradeoff in integra...
Bo Wu, Ram Nevatia
RSCTC
2010
Springer
142views Fuzzy Logic» more  RSCTC 2010»
13 years 5 months ago
Learning from Imbalanced Data in Presence of Noisy and Borderline Examples
In this paper we studied re-sampling methods for learning classifiers from imbalanced data. We carried out a series of experiments on artificial data sets to explore the impact of ...
Krystyna Napierala, Jerzy Stefanowski, Szymon Wilk
IS
2008
13 years 7 months ago
Mining relational data from text: From strictly supervised to weakly supervised learning
This paper approaches the relation classification problem in information extraction framework with different machine learning strategies, from strictly supervised to weakly superv...
Zhu Zhang
SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 10 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
CVPR
2006
IEEE
14 years 9 months ago
Applying Ensembles of Multilinear Classifiers in the Frequency Domain
Ensemble methods such as bootstrap, bagging or boosting have had a considerable impact on recent developments in machine learning, pattern recognition and computer vision. Theoret...
Christian Bauckhage, Thomas Käster, John K. T...