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https://ieeexplore.ieee.org/document/5178915/
Creating an ensemble of diverse support vector machines using Adaboost Abstract: Support vector machines are one of the most employed methods of pattern classification, and the Adaboost algorithm is an effective way of improving the performance of the weak learners that compose the ensemble.Cited by: 14
https://dl.acm.org/citation.cfm?id=1704608
Home Browse by Title Proceedings IJCNN'09 Creating an ensemble of diverse support vector machines using adaboost. Article . Creating an ensemble of diverse support vector machines using adaboost. Share on. Authors: Naiyan Hari Cândido Lima. Departament of Computer Engineering and Automation, Universidade Federal do Rio Grande do Norte .Cited by: 14
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000005178915
Support vector machines are one of the most employed methods of pattern classification, and the Adaboost algorithm is an effective way of improving the performance of the weak learners that compose the ensemble. In this article, we propose to create an Adaboost-based ensemble of SVM, by altering the Gaussian width parameter of the RBF-SVM.
http://web.eecs.utk.edu/~leparker/Courses/CS425-528-fall10/Handouts/AdaboostSVM(Journal).pdf
of support vector machines for the development of SVM-based ensemble methods. Journal of Machine Learning Research 5, 725–775] that Adaboost with heterogeneous SVMs could work well. Moreover, we extend AdaBoostSVM to the Diverse AdaBoostSVM to address the reported accuracy/diversity dilemma of the original Adaboost.
https://www.kdnuggets.com/2019/09/ensemble-methods-machine-learning-adaboost.html
AdaBoost, which stays for ‘Adaptive Boosting’, is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. In this article, I’m going to provide an idea of the maths behind Adaboost, plus I’ll provide an implementation in Python.
https://datasciencechalktalk.com/2019/09/07/ensemble-methods-for-machine-learning-adaboost/
Sep 07, 2019 · AdaBoost, which stays for ‘Adaptive Boosting’, is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. In this article, I’m going to provide an idea of the maths behind Adaboost, plus I’ll provide an implementation in Python.
https://www.sciencedirect.com/science/article/abs/pii/S0950705116301605
Sep 01, 2016 · In this paper, we focus on the DL research based on Support Vector Machine (SVM), and first present an Ex-Adaboost learning strategy, and then propose a new Deep Support Vector Machine (called DeepSVM).Cited by: 19
https://www.sciencedirect.com/science/article/pii/S0950705119303533
Nov 15, 2019 · With the use of a bagged ensemble comprising of support vector machines for classification, this accuracy is further enhanced by roughly 5%. Finally, from the observations of the AdaBoost ensemble’s performance compared with the others (in Table 4 , Table 5 , Table 6 ), it can be inferred that our bagged model gives a better performance than ...Cited by: 1
https://machinelearningmastery.com/boosting-and-adaboost-for-machine-learning/
Boosting is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. In this post you will discover the AdaBoost Ensemble method for machine learning. After reading this post, you will know: What the boosting ensemble method is and generally how it works. How to learn to boost decision …
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