Find all needed information about Boosting Support Vector Machines Successfully. Below you can see links where you can find everything you want to know about Boosting Support Vector Machines Successfully.
https://link.springer.com/chapter/10.1007/978-3-642-02326-2_51
Boosting has been shown to improve the predictive performance of unstable learners such as decision trees, but not of stable learners like support vector machines (SVM). In addition to the model stability problem, the high computational cost of SVM prohibits it from generating multiple models to form an ensemble for large data sets.Cited by: 9
https://elkingarcia.github.io/Papers/MLDM07.pdf
port Vector Machines classifiers combined with Boosting techniques. This classifier presents a better performance in training time, a similar gener-alization and a similar model complexity but the model representation is more compact. 1 Introduction Support Vector Machines (SVM) have been applied successfully in many prob-
http://www.datalab.uci.edu/papers/pavlovd-icpr2000.pdf
Scaling-up Support Vector Machines Using Boosting Algorithm ... In the recent years supportvector machines (SVMs) have been successfully applied to solve a large number of clas-sification problems. Training an SVM, usually posed as ... Keywords: Support …
https://stats.stackexchange.com/questions/73526/boosting-with-linear-svm
In Wang's et al's Boosting Support Vector Machines for Imbalanced Data Sets, they applied a slightly different formula in the weight update, aiming at dealing with class imbalance. Weights for data instances instruction is also shown in Prof. Lin's website. Hope it helps.
https://pdfs.semanticscholar.org/21d9/f2a0e17a62320c5b76497dea2e2f5fd59114.pdf
boosting and bagging with neural networks as base classifiers, as well as support vector machines and logistic regression models, to binary prediction problems with financial time series data. For boosting, we use a modified boosting algorithm that does not require a weak learner as the base classifier.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4408786/
Boosting support vector machines successfully. In Mult Classifier Syst volume 5519 of Lect Notes Comput Sci (ed. Benediktsson J, et al. ), pp. 509–518. Tyagi S, Vaz C, Gupta V, Bhatia R, Maheshwari S, Srinivasan A, Bhattacharya A 2008. CID-miRNA: a web server for prediction of novel miRNA precursors in human genome.Cited by: 17
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
https://pdfs.semanticscholar.org/0514/bf587f4fcfb77057796506c58d66ae3cff09.pdf
Neural networks have been successfully applied to the problem of DOA estimation and adaptive beamforming in [4], [5], [6]. New machine learning techniques, such as support vector machines (SVM) and boosting, perform ex-ceptionally well in multiclass problems and new optimization techniques are published regularly.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.2688
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In the recent years support vector machines (SVMs) have been successfully applied to solve a large number of classification problems. Training an SVM, usually posed as a quadratic programming (QP) problem, often becomes a challenging task for the large data sets due to the high memory requirements and slow …
https://www.sciencedirect.com/science/article/pii/S0263786311001256
This paper outlines the development of artificial neural networks ensemble and support vector machines classification models to predict project cost and schedule success, using status of early planning as the model inputs. Through industry survey, early planning and project performance information from a total of 92 building projects is collected.Cited by: 33
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