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ML
2008
ACM
134views Machine Learning» more  ML 2008»
14 years 11 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...
84
Voted
PAMI
2006
185views more  PAMI 2006»
14 years 11 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...
94
Voted
MVA
2007
186views Computer Vision» more  MVA 2007»
14 years 11 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
97
Voted
PRL
2007
166views more  PRL 2007»
14 years 11 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
14 years 9 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...