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https://www.sciencedirect.com/science/article/pii/S0169743915001574
Aug 15, 2015 · Weighted variable kernel matrix can be constructed by incorporating the variable importance into primary kernel. Thus, the constructed kernel explicitly makes full use of information variables and neglect the effect of those noisy variables, and each variable is given the different weight.Cited by: 7
https://www.sciencedirect.com/science/article/pii/S003991400900798X
Mar 15, 2010 · 2.1.1. Optimized wavelength-weighted least-squares support vector machine (VWLS-SVM) The least-squares support vector machine (LS-SVM), a relatively new algorithm from the machine learning community, has been described in detail by Suykens et al. , . It has attracted great attention and gained extensive application in pattern recognition and ...Cited by: 22
https://datascienceplus.com/weighted-linear-support-vector-machine/
Apr 06, 2017 · Notice that the proportion of spam and ham in the training data set is similar to that of the entire data. One of the widely used classifiers is Linear Support Vector Machine. From my last writing on Linear Support Vector Machine, you can find that in case of Linear SVM we solve the following optimization problem.Author: Ananda Das
https://www.iis.sinica.edu.tw/page/jise/2014/201411_06.pdf
give different penalties to the samples depending on their positions [14]. So a weighted least squares twin support vector machines for pattern classification, termed as WLST- SVM, was presented in [10]. Different penalty coefficients ν1i were brought to the nega-tive samples according to the value of ξi, which satisfy equality constraint ...
http://users.stat.umn.edu/~wangx346/research/SVM_selection.pdf
The support vector machine (SVM) is a powerful binary classification tool with high accuracy and great flexibility. It has achieved great success, but its performance can be seriously impaired if many redundant covariates are included. Some efforts have been devoted to studying variable selection for SVMs, but asymptotic properties, such as variable
https://dataaspirant.com/2017/01/19/support-vector-machine-classifier-implementation-r-caret-package/
Jan 19, 2017 · The principle behind an SVM classifier (Support Vector Machine) algorithm is to build a hyperplane separating data for different classes. This hyperplane building procedure varies and is the main task of an SVM classifier.
https://www.mathworks.com/help/stats/classreg.learning.classif.compactclassificationsvm.loss.html
Response variable name, specified as the name of a variable in TBL. You must specify ResponseVarName as a character vector or string scalar. For example, if the response variable Y is stored as TBL.Y, then specify ResponseVarName as 'Y'.Otherwise, the software treats all columns of TBL, including Y, as predictors when training the model.
https://stats.stackexchange.com/questions/39243/how-does-one-interpret-svm-feature-weights
How does one interpret SVM feature weights? Ask Question Asked 7 years, ... Selecting the most relevant variables is usually suboptimal for building a predictor, particularly if the variables are redundant. Conversely, a subset of useful variables may exclude many redundant, but relevant, variables." ... How does a Support Vector Machine (SVM ...
https://scikit-learn.org/stable/modules/svm.html
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. For optimal performance, use C-ordered numpy.ndarray (dense) or scipy.sparse.csr_matrix (sparse) with dtype=float64. 1.4.1.
https://nlp.stanford.edu/IR-book/html/htmledition/support-vector-machines-the-linearly-separable-case-1.html
Support vector machines: The linearly separable case Figure 15.1: The support vectors are the 5 points right up against the margin of the classifier. For two-class, separable training data sets, such as the one in Figure 14.8 (page ), there are lots of possible linear separators.
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