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http://www.hpl.hp.com/techreports/2002/HPL-2002-354R1.pdf
We present an algorithm for selecting support vector machine (SVM) meta-parameter values which is based on ideas from design of experiments (DOE) and demonstrate that it is robust and works effectively and efficiently on a variety of problems.
https://www.researchgate.net/publication/2906127_Parameter_Selection_for_Support_Vector_Machines
In this paper, we propose a support vector machine (SVM) meta-parameter optimization method which uses sequential number theoretic optimization (SNTO) and gradient information for better ...Author: Carl Staelin
https://www.sciencedirect.com/science/article/pii/S187802961100908X
Support Vector Machine (SVM) is a new modeling method. It has shown good performance in many field and mostly outperformed neural networks. The parameter selection should to be done before training SVM. Modified particle swarm optimization (POS) was adpoted to select parameters of SVM.Cited by: 13
https://journals.sagepub.com/doi/full/10.1260/1748-3018.8.2.163
Jun 01, 2014 · Parameter selection for kernel functions is important to the robust classification performance of a support vector machine (SVM). This paper introduces a parameter selection method for kernel functions in SVM. The proposed method tries to estimate the class separability by cosine similarity in the kernel space.Cited by: 3
https://link.springer.com/chapter/10.1007/978-3-642-45111-9_21
Parameter selection greatly impacts the classification accuracy of Support Vector Machines (SVM). However, this step is often overlooked in experimental comparisons, for it is time consuming and requires familiarity with the inner workings of SVM.Cited by: 12
https://www.sciencedirect.com/science/article/pii/S1568494619301632
Support vector machine (SVM) has been recently considered as one of the most efficient classifiers. However, the time complexity of kernel SVM, which is quadratic in the number of training patterns, makes it impractical to be applied to large data sets.Cited by: 1
http://www.chapelle.cc/olivier/pub/mlj02.pdf
CHOOSING MULTIPLE PARAMETERS FOR SUPPORT VECTOR MACHINES 133 Note however that according to the theorem the average performance depends on the ratio E{R 2/γ} and not simply on the large margin γ. Why multiple parameters? The SVM algorithm usually depends on several parameters.
https://www.hindawi.com/journals/cin/2017/7273017/
Using this approach, an output power prediction model of a grid-connected PV system is proposed in this paper to optimize the support vector machine (SVM) with the artificial bee colony algorithm. 5.1. Optimal Parameter Selection for the SVM Model Based on ABC AlgorithmCited by: 1
http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/6330/pdf/imm6330.pdf
parameter selection, three of such methods will be selected for implementation and application, first to data sets available at machine learning repositories and then to the concrete problem of building SVR models to predict wind energy production at individual farms.
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