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https://cran.r-project.org/web/packages/e1071/vignettes/svmdoc.pdf
Support Vector Machines * The Interface to libsvm in package e1071 by David Meyer FH Technikum Wien, Austria [email protected] November 25, 2019 \Hype or Hallelujah?" is the provocative title used byBennett & Campbell (2000) in an overview of Support Vector Machines …
https://www.r-bloggers.com/machine-learning-using-support-vector-machines/
Apr 19, 2017 · Support Vector Machines (SVM) is a data classification method that separates data using hyperplanes. The concept of SVM is very intuitive and easily understandable. If we have labeled data, SVM can be used to generate multiple separating hyperplanes such that the data space is divided into segments and each segment contains only one kind of … Continue reading Machine Learning Using Support ...
https://cran.r-project.org/web/packages/kernlab/index.html
Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.
https://cran.r-project.org/web/packages/parallelSVM/parallelSVM.pdf
2 parallelSVM-package parallelSVM-package Parallel-voting version of Support-Vector-Machine Description By sampling your data, running the Support-Vector-Machine algorithm on these samples in par-allel on your own machine and letting your models vote on a prediction, we return much faster
https://www.edureka.co/blog/support-vector-machine-in-r/
Support Vector Machine In R: With the exponential growth in AI, Machine Learning is becoming one of the most sort after fields.As the name suggests, Machine Learning is the ability to make machines learn through data by using various Machine Learning Algorithms and in this blog on Support Vector Machine In R, we’ll discuss how the SVM algorithm works, the various features of SVM and how it ...Author: Zulaikha Lateef
https://www.youtube.com/watch?v=iu9rrCbSqgM
May 05, 2015 · Reference: (Book) An Introduction to Statistical Learning with Applications in R (Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani)Author: Hamed Hasheminia
http://www.di.fc.ul.pt/~jpn/r/svm/svm.html
The separating hyperplane can be written as \[w.x + b = 0\] where vector b is the bias, and w the weigths. w is the normal to the hyperplane and \(\frac{b}{\w\}\) …
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