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ML
2008
ACM
134views Machine Learning» more  ML 2008»
13 years 9 months ago
Multilabel classification via calibrated label ranking
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operat...
Johannes Fürnkranz, Eyke Hüllermeier, En...
PAMI
2006
185views more  PAMI 2006»
13 years 9 months ago
Generic Object Recognition with Boosting
This paper explores the power and the limitations of weakly supervised categorization. We present a complete framework that starts with the extraction of various local regions of e...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...
MVA
2007
186views Computer Vision» more  MVA 2007»
13 years 9 months ago
Probabilistic-topological calibration of widely distributed camera networks
Abstract We propose a method for estimating the topology of distributed cameras, which can provide useful information for multi-target tracking in a wide area, without object ident...
Norimichi Ukita
PRL
2007
166views more  PRL 2007»
13 years 9 months ago
Boosted Landmarks of Contextual Descriptors and Forest-ECOC: A novel framework to detect and classify objects in cluttered scene
In this paper, we present a novel methodology to detect and recognize objects in cluttered scenes by proposing boosted contextual descriptors of landmarks in a framework of multi-...
Sergio Escalera, Oriol Pujol, Petia Radeva
ICPR
2010
IEEE
13 years 7 months ago
Boosting Bayesian MAP Classification
In this paper we redefine and generalize the classic k-nearest neighbors (k-NN) voting rule in a Bayesian maximum-a-posteriori (MAP) framework. Therefore, annotated examples are u...
Paolo Piro, Richard Nock, Frank Nielsen, Michel Ba...