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http://www.cs.toronto.edu/~rgrosse/courses/csc2515_2019/readings/SVM-and-boosting.pdf
Support Vector Machines and Boosting Roger Grosse 1 Introduction We now introduce two additional algorithms which build on the principles we’ve just covered. Support vector machines (SVMs) are another kind of linear classi er, and before the deep learning revolution, they were one of the best general-purpose machine learning algorithms.
https://elkingarcia.github.io/Papers/MLDM07.pdf
3 Boosting Support Vector Machines The running time of training algorithms for SVMs can be reduced if only a few training examples are involved in the actual computations. This fact can be exploited by Adaboost if at each iteration most of the weight in the distribution passed to the weak learner is assigned to a few data points.
https://www.researchgate.net/publication/221506159_Boosting_Support_Vector_Machines
C. BOOSTING SUPPORT VECTOR MACHINES. Luego de tener un alg oritmo debilitado para SVM, se . ... However, by boosting an underlying classifier of appropriately low strength, we are able to boost ...
http://www.csi.uottawa.ca/~nat/Papers/29-Wang.pdf
cus on support vector machines, which have demonstrated remarkable success in many different applications. Our experiments show that boosting methods can be combined with SVMs very effectively in the presence of imbalanced data. Our results show that this method is not only able to solve the skewed vector spaces problem, but also the
https://www.sciencedirect.com/science/article/pii/S1877050918322269
INNS Conference on Big Data and Deep Learning 2018 Boosting Support Vector Machines for Imbalanced Microarray Data Risky Frasetio Wahyu Pratamaa, Santi Wulan Purnamia,* and Santi Puteri Rahayua a Department of Statistics, Institut Teknologi Sepuluh Nopember, Sukolilo, Surabaya 60111, Indonesia Abstract Nowadays, microarray data plays an ...Cited by: 1
https://web.stanford.edu/~hastie/Papers/ASA2003.pdf
August 2003 Trevor Hastie, Stanford Statistics 1 Boosting and Support Vector Machines Trevor Hastie Statistics Department Stanford University Collaborators: …
https://web.stanford.edu/~hastie/TALKS/svm.pdf
Support Vector Machines, Kernel Logistic Regression, and Boosting Trevor Hastie Statistics Department Stanford University Collaborators: Brad Efron, …
https://elkingarcia.github.io/Papers/MLDM07.pdf
3 Boosting Support Vector Machines The running time of training algorithms for SVMs can be reduced if only a few training examples are involved in the actual computations. This fact can be exploited by Adaboost if at each iteration most of the weight in the distribution passed to the weak learner is assigned to a few data points.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3103196/
May 27, 2011 · We evaluated the predictive accuracy of random forests (RF), stochastic gradient boosting (boosting) and support vector machines (SVMs) for predicting genomic breeding values using dense SNP markers and explored the utility of RF for ranking the predictive importance of markers for pre-screening markers or discovering chromosomal locations of QTLs.Cited by: 119
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
http://www.cs.toronto.edu/~rgrosse/courses/csc2515_2019/readings/SVM-and-boosting.pdf
Support Vector Machines and Boosting Roger Grosse 1 Introduction We now introduce two additional algorithms which build on the principles we’ve just covered. Support vector machines (SVMs) are another kind of linear classi er, and before the deep learning revolution, they were one of the best general-purpose machine learning algorithms.
https://www.researchgate.net/publication/225509786_Boosting_Support_Vector_Machines_for_Imbalanced_Data_Sets
We use support vector machines with soft margins as the base classifier to solve the skewed vector spaces problem. We then counter the excessive bias introduced by this approach with a boosting ...
https://www.researchgate.net/publication/221506159_Boosting_Support_Vector_Machines
This approach first converts a regression sample to a binary classification sample from a geometric point of view, and performs AdaBoost with support vector machines base learner on the converted ...
https://www.sciencedirect.com/science/article/pii/S0010482518302245
Despite generation of extensive clinical data obtained from the high-throughput technologies, it is necessary to develop machine learning algorithms to guide the prediction process. In the study, we utilize boosting and develop three computational methods to increase the performance of support vector machines (SVM).Cited by: 4
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