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CVPR
2012
IEEE
7 years 3 months ago
Contextual Boost for Pedestrian Detection
Pedestrian detection from images is an important and yet challenging task. The conventional methods usually identify human figures using image features inside the local regions. In...
Yuanyuan Ding, Jing Xiao
ICASSP
2011
IEEE
8 years 3 months ago
Application specific loss minimization using gradient boosting
Gradient boosting is a flexible machine learning technique that produces accurate predictions by combining many weak learners. In this work, we investigate its use in two applica...
Bin Zhang, Abhinav Sethy, Tara N. Sainath, Bhuvana...
CORR
2011
Springer
127views Education» more  CORR 2011»
8 years 3 months ago
Generalized Boosting Algorithms for Convex Optimization
Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks,...
Alexander Grubb, J. Andrew Bagnell
JRTIP
2010
137views more  JRTIP 2010»
8 years 6 months ago
Cascaded online boosting
In this paper, we propose a cascaded version of the online boosting algorithm to speed-up the execution time and guarantee real-time performance even when employing a large number ...
Ingrid Visentini, Lauro Snidaro, Gian Luca Foresti
PAMI
2011
8 years 7 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
BMVC
2010
8 years 9 months ago
StyP-Boost: A Bilinear Boosting Algorithm for Learning Style-Parameterized Classifiers
We introduce a novel bilinear boosting algorithm, which extends the multi-class boosting framework of JointBoost to optimize a bilinear objective function. This allows style param...
Jonathan Warrell, Philip H. S. Torr, Simon Prince
ICPR
2010
IEEE
8 years 10 months ago
A Re-evaluation of Pedestrian Detection on Riemannian Manifolds
Abstract--Boosting covariance data on Riemannian manifolds has proven to be a convenient strategy in a pedestrian detection context. In this paper we show that the detection perfor...
Diego Tosato, Michela Farenzena, Marco Cristani, V...
FOCS
2010
IEEE
8 years 10 months ago
Boosting and Differential Privacy
Boosting is a general method for improving the accuracy of learning algorithms. We use boosting to construct improved privacy-preserving synopses of an input database. These are da...
Cynthia Dwork, Guy N. Rothblum, Salil P. Vadhan
IR
2010
8 years 10 months ago
Adapting boosting for information retrieval measures
Abstract We present a new ranking algorithm that combines the strengths of two previous methods: boosted tree classification, and LambdaRank, which has been shown to be empiricall...
Qiang Wu, Christopher J. C. Burges, Krysta Marie S...
PRL
2007
138views more  PRL 2007»
8 years 11 months ago
Ent-Boost: Boosting using entropy measures for robust object detection
Recently, boosting has come to be used widely in object-detection applications because of its impressive performance in both speed and accuracy. However, learning weak classifier...
Duy-Dinh Le, Shin'ichi Satoh
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