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Optimal and Adaptive Algorithms for Online Boosting [article]

Alina Beygelzimer, Satyen Kale, Haipeng Luo
2015 arXiv   pre-print
This optimal algorithm is not adaptive however. Using tools from online loss minimization, we derive an adaptive online boosting algorithm that is also parameter-free, but not optimal.  ...  Based on a novel and natural definition of weak online learnability, we develop two online boosting algorithms. The first algorithm is an online version of boost-by-majority.  ...  Algorithm N T Optimal? Adaptive? Online BBM (Section 3.1) Lemma 5.  ... 
arXiv:1502.02651v1 fatcat:svaj4rrgxfgfxbbzta54l5vuq4

Incremental Learning of Boosted Face Detector

Chang Huang, Haizhou Ai, Takayoshi Yamashita, Shihong Lao, Masato Kawade
2007 2007 IEEE 11th International Conference on Computer Vision  
By this means, the offline learned general-purpose detectors can be adapted to special online situations at a low extra cost, and still retains good generalization ability for common environments.  ...  To alleviate this problem, this paper proposes an incremental learning algorithm to effectively adjust a boosted strong classifier with domain-partitioning weak hypotheses to online samples, which adopts  ...  each weak hypothesis as those online boosting algorithms introduced in section 1, and thus achieves only stage-wise optimization.  ... 
doi:10.1109/iccv.2007.4408850 dblp:conf/iccv/HuangAYLK07 fatcat:w4p6wdie2fdpplgnnzw2eoqn4y

Learning non-homogenous textures and the unlearning problem with application to drusen detection in retinal images

Noah Lee, Andrew F. Laine, Theodore R. Smith
2008 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
We perform probabilistic boosting and structural similarity clustering for fast selective learning in a large knowledge domain acquired over different time steps.  ...  In this work we present a novel approach for learning nonhomogenous textures without facing the unlearning problem.  ...  ACKNOWLEDGEMENTS This work was founded by NEI (R01 EY015520-01), the NYC Community Trust (RTS), and unrestricted funds from Research to Prevent Blindness.  ... 
doi:10.1109/isbi.2008.4541221 dblp:conf/isbi/LeeLS08 fatcat:f3cgrouolvht3hdexvk4d5skoy

Data level object detector adaptation with online multiple instance samples

Bobo Zeng, Guijin Wang, Zhiwei Ruan, Xinggang Lin
2012 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
We re-derive an efficient MILBoost by eliminating line search in optimization and introduce it to collect online multiple instance samples, which don't require strict sample alignment.  ...  The adapted detector has good adaptation ability, while maintaining its generalization ability as well.  ...  The original MILBoost needs line search in the optimization and slow, so we re-derive a improved MIL-Boost algorithm.  ... 
doi:10.1109/icassp.2012.6288152 dblp:conf/icassp/ZengWRL12 fatcat:cwt7ya2lhbfgpatjkq7mzvzwge

Online Agnostic Boosting via Regret Minimization [article]

Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran
2020 arXiv   pre-print
Our algorithm is based on an abstract (and simple) reduction to online convex optimization, which efficiently converts an arbitrary online convex optimizer to an online booster.  ...  In this work we provide the first agnostic online boosting algorithm; that is, given a weak learner with only marginally-better-than-trivial regret guarantees, our algorithm boosts it to a strong learner  ...  Thus, in fact we obtain a family of boosting algorithms; one for each choice of an online convex optimizer. Specifically, Theorem 2 follows by picking Online Gradient Decent for the meta-algorithm.  ... 
arXiv:2003.01150v1 fatcat:7rfda3kyabgovdlysijnuccrbe

Unsupervised and Online Update of Boosted Temporal Models: The UAL2Boost

Pedro Canotilho Ribeiro, Plinio Moreno, Jose Santos-Victor
2010 2010 Ninth International Conference on Machine Learning and Applications  
We address an automatic update procedure of the L2boost algorithm that is able to adapt the initial models learned off-line.  ...  the learning algorithm to each scenario.  ...  We address the general problem of online unsupervised adaptation of the base learners of the L 2 boosting algorithm [10] and apply the algorithm on the unsupervised adaptation of models for human action  ... 
doi:10.1109/icmla.2010.143 dblp:conf/icmla/RibeiroMS10 fatcat:3qpurehbfvhxzja35j6fvnanu4

Adaptive Kernel in Meshsize Boosting Algorithm in KDE

CC Ishiekwene, E Nwelih
2011 African Research Review  
This paper proposes the use of adaptive kernel in a meshsize boosting algorithm in kernel density estimation.  ...  An empirical study for this scheme is conducted and the findings are comparatively attractive.  ...  The need to use a meshsize in place of the leave-one-out lies on the fact that boosting is like the steepest-descent algorithm in unconstrained optimization and thus a good substitute that approximates  ... 
doi:10.4314/afrrev.v5i1.64541 fatcat:nyerkrhv3vfqpdqv6fgynn7i4u

A Novel Family of Boosted Online Regression Algorithms with Strong Theoretical Bounds [article]

Dariush Kari and Farhan Khan and Selami Ciftci and Suleyman Serdar Kozat
2016 arXiv   pre-print
We investigate boosted online regression and propose a novel family of regression algorithms with strong theoretical bounds.  ...  We demonstrate an intrinsic relationship, in terms of boosting, between the adaptive mixture-of-experts and data reuse algorithms.  ...  Acknowledgments This work is supported in part by Turkish Academy of Sciences Outstanding Researcher Programme, TUBITAK Contract No. 113E517, and Turk Telekom Communications Services Incorporated.  ... 
arXiv:1601.00549v2 fatcat:5eaak4sevvc5fjlpq4r5k4hizu

Boosted adaptive filters

Dariush Kari, Ali H. Mirza, Farhan Khan, Huseyin Ozkan, Suleyman S. Kozat
2018 Digital signal processing (Print)  
We demonstrate an intrinsic relationship, in terms of boosting, between the adaptive mixture-of-experts and data reuse algorithms.  ...  Additionally, we introduce a boosting algorithm based on random updates that is significantly faster than the conventional boosting methods and other variants of our proposed algorithms while achieving  ...  Recently, in [27] , the authors have developed two online boosting algorithms for classification, an optimal algorithm in terms of the number of weak learners, and also an adaptive algorithm using the  ... 
doi:10.1016/j.dsp.2018.07.012 fatcat:c53o56iewfhgvbsd2bpsm2xbbq

Online active supervision of an evolving classifier for customized-gesture-command learning

Manuel Bouillon, Eric Anquetil
2017 Neurocomputing  
This paper presents a novel approach for the online active learning of gesture commands, with three contributions. The IntuiSup supervisor monitors the learning process and user interactions.  ...  The Boosted-ESU (B-ESU) method optimizes interaction impact to fasten system learning speed. The efficiency of our approach is evaluated on the publicly available ILG Data Base of gesture commands.  ...  They use online learning algorithms to adapt to the data flow and cope with class adding (or removal) at run-time.  ... 
doi:10.1016/j.neucom.2016.12.094 fatcat:codmdsurf5akddiq3yeruvxeme

Online coordinate boosting

Raphael Pelossof, Michael Jones, Ilia Vovsha, Cynthia Rudin
2009 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops  
We present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire's AdaBoost algorithm than previous online boosting  ...  Abstract We present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire's AdaBoost algorithm than previous online  ...  Our goal is to create a fast and accurate online learning algorithm that can adapt an existing boosted classifier to a new environment and concept change.  ... 
doi:10.1109/iccvw.2009.5457454 dblp:conf/iccvw/PelossofJVR09 fatcat:msxr2aa7uvewroqzcdcafct5b4

Online Boosting Algorithms for Multi-label Ranking [article]

Young Hun Jung, Ambuj Tewari
2018 arXiv   pre-print
We design online boosting algorithms with provable loss bounds for multi-label ranking.  ...  We also design an adaptive algorithm that does not require this knowledge and is hence more practical.  ...  -1452099 and CIF-1422157 and of the Sloan Foundation via a Sloan Research Fellowship.  ... 
arXiv:1710.08079v2 fatcat:v4lktqtv5nhsnk5f7wuwlyxa3m

Taylor expansion based classifier adaptation: Application to person detection

Cha Zhang, Raffay Hamid, Zhengyou Zhang
2008 2008 IEEE Conference on Computer Vision and Pattern Recognition  
We demonstrate this property on two popular classifiers (logistic regression and boosting), while using two types of user labels (direct labels and similarity labels).  ...  In this paper, we present a general framework for classifier adaptation, which improves an existing generic classifier in the new test environment.  ...  As for the adaptation algorithms used after new examples are obtained, [18] used an online Winnow algorithm to update the classifier; [12, 21] used online boosting based on the work in [19] .  ... 
doi:10.1109/cvpr.2008.4587801 dblp:conf/cvpr/ZhangHZ08 fatcat:zpco6ceexzb3dgivpftu77koty

Tracking-by-Segmentation with Online Gradient Boosting Decision Tree

Jeany Son, Ilchae Jung, Kayoung Park, Bohyung Han
2015 2015 IEEE International Conference on Computer Vision (ICCV)  
We propose an online tracking algorithm that adaptively models target appearances based on an online gradient boosting decision tree.  ...  Our algorithm is particularly useful for non-rigid and/or articulated objects since it handles various deformations of the target effectively by integrating a classifier operating on individual patches  ...  Conclusions We proposed a novel online learning algorithm for gradient boosting decision tree to track and segment non-rigid and deformable objects.  ... 
doi:10.1109/iccv.2015.350 dblp:conf/iccv/SonJPH15 fatcat:idoazofhcfexvajzd72ebljqli

Online Coordinate Boosting [article]

Raphael Pelossof, Michael Jones, Ilia Vovsha, Cynthia Rudin
2008 arXiv   pre-print
We present a new online boosting algorithm for adapting the weights of a boosted classifier, which yields a closer approximation to Freund and Schapire's AdaBoost algorithm than previous online boosting  ...  We also contribute a new way of deriving the online algorithm that ties together previous online boosting work.  ...  Our goal is to create a fast and accurate online learning algorithm that can adapt an existing boosted classifier to a new environment and concept change.  ... 
arXiv:0810.4553v1 fatcat:lys55fy6tjdmndbd72tc2zfbee
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