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2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost.
Zhen James Xiang, Peter J. Ramadge
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We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers. The proposed algorithm is computationally efficient and in test examples produces composite classifiers that are sparser and generalize as well those produced by Adaboost.
Zhen James Xiang, Peter J. Ramadge
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2017
Machine learning is becoming prevalent in all aspects of our lives. For some applications, there is a need for simple but accurate white-box systems that are able to train efficiently and with little data. "Boosting" is an intuitive method, combining many simple (possibly inaccurate) predictors to form a powerful, accurate classifier.
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Machine learning is becoming prevalent in all aspects of our lives. For some applications, there is a need for simple but accurate white-box systems that are able to train efficiently and with little data. "Boosting" is an intuitive method, combining many simple (possibly inaccurate) predictors to form a powerful, accurate classifier.
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2007
Following [4], we analyze boosting from a game-theoretic perspective. We define a wide class of boosting classification algorithms called H-boosting methods, which are based on Hannan-consistent game playing strategies. These strategies tend to minimize the regret of a player, i.e.
Anna Gambin, Ewa Szczurek
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Following [4], we analyze boosting from a game-theoretic perspective. We define a wide class of boosting classification algorithms called H-boosting methods, which are based on Hannan-consistent game playing strategies. These strategies tend to minimize the regret of a player, i.e.
Anna Gambin, Ewa Szczurek
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Proceedings of the 24th international conference on Machine learning, 2007
A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic principles, which exploit the statistical interpretation of boosting to determine a principled extension of the boosting loss.
Hamed Masnadi-Shirazi, Nuno Vasconcelos
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A cost-sensitive extension of boosting, denoted as asymmetric boosting, is presented. Unlike previous proposals, the new algorithm is derived from sound decision-theoretic principles, which exploit the statistical interpretation of boosting to determine a principled extension of the boosting loss.
Hamed Masnadi-Shirazi, Nuno Vasconcelos
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To Boost or Not to Boost Residents and Fellows—That Is the Question
Journal of Graduate Medical Education, 2022Sofia Zavala +2 more
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2005
Martingale boosting is a simple and easily understood technique with a simple and easily understood analysis. A slight variant of the approach provably achieves optimal accuracy in the presence of random misclassification noise.
Philip M. Long, Rocco A. Servedio
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Martingale boosting is a simple and easily understood technique with a simple and easily understood analysis. A slight variant of the approach provably achieves optimal accuracy in the presence of random misclassification noise.
Philip M. Long, Rocco A. Servedio
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2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005
In this paper, we described an efficient feature pursuit scheme for boosting. The proposed method is based on the infomax principle, which seeks optimal feature that achieves maximal mutual information with class labels. Direct feature pursuit with infomax is computationally prohibitive, so an efficient gradient ascent algorithm is further proposed ...
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In this paper, we described an efficient feature pursuit scheme for boosting. The proposed method is based on the infomax principle, which seeks optimal feature that achieves maximal mutual information with class labels. Direct feature pursuit with infomax is computationally prohibitive, so an efficient gradient ascent algorithm is further proposed ...
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MCED Blood Test Boosts Cancer Detection in Symptomatic Patients
Ca-A Cancer Journal for Clinicians, 2023Mike Fillon
exaly
American Journal of Orthodontics and Dentofacial Orthopedics
Thijs Becker +4 more
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Thijs Becker +4 more
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