Results 221 to 230 of about 330,663 (269)
Long-term immune response to mRNA anti-SARS-CoV-2 vaccination in patients with cancer. [PDF]
Front ImmunolLobo-Martins S +24 more
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Quality assurance incorporating artificial intelligence-generated reference contours in a phase II radiotherapy trial. [PDF]
Phys Imaging Radiat OncolLee W +8 more
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A predictive model for rapid assessment of protective efficacy against emerging SARS-CoV-2 variants. [PDF]
Infect Dis ModelJin L +5 more
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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
openaire +1 more source
To boost or not to boost, and how to do it
International Journal of Radiation Oncology*Biology*Physics, 1991Breast cancer, Radiotherapy, Boost, Treatment planning. The function of a “boost” dose in radiotherapy is to give a higher dose to an area thought to have a higher tumor burden than the surrounding tissue. By restricting the high-dose region to a part of the target volume rather than the entire volume, the use of a boost might reduce the incidence of ...
Abram Recht, Jay R. Harris
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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
openaire +1 more source
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.
openaire +1 more source
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.
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source