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Machine learning for multiple sclerosis classification and disability prediction using clinical and MRI data. [PDF]
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Strain Engineering: A Boosting Strategy for Photocatalysis
Advances in Materials, 2022Whilst the photocatalytic technique is considered to be one of the most significant routes to address the energy crisis and global environmental challenges, the solar‐to‐chemical conversion efficiency is still far from satisfying practical industrial ...
Yingxuan Miao +4 more
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Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
Construction and Building Materials, 2020In this paper, an intelligent approach based on the machine learning technique is proposed for predicting the compressive strength of concrete. This approach employs the adaptive boosting algorithm to construct a strong learner by integrating several ...
D. Feng +6 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
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Applied Soft Computing, 2020
The investigation of the accuracy of methods employed to forecast agricultural commodities prices is an important area of study. In this context, the development of effective models is necessary. Regression ensembles can be used for this purpose.
Matheus Ribeiro, L. Coelho
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The investigation of the accuracy of methods employed to forecast agricultural commodities prices is an important area of study. In this context, the development of effective models is necessary. Regression ensembles can be used for this purpose.
Matheus Ribeiro, L. Coelho
semanticscholar +1 more source
Information Fusion, 2020
Ensembles, especially ensembles of decision trees, are one of the most popular and successful techniques in machine learning. Recently, the number of ensemble-based proposals has grown steadily.
Sergio González +4 more
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Ensembles, especially ensembles of decision trees, are one of the most popular and successful techniques in machine learning. Recently, the number of ensemble-based proposals has grown steadily.
Sergio González +4 more
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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
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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.
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