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https://www.futurelearn.com/courses/data-mining-with-weka/0/steps/25399
In essence, support vector machines drive a straight line between two classes, right down the middle of the channel – which you can see using Weka’s boundary visualizer. If the classes cannot be separated by a straight line, a device called the “kernel trick” enables support vector machines to make boundaries of different shapes, not just straight lines.
http://www.cbs.dtu.dk/courses/27623.algo/exercises/ex_SVM/SVM_weka_intro.php
weka That should open the GUI of weka. Next go to "Applications -> Explorer". Open the file herg_vol_5probes_201train.arff. How many class, attributes and instances do you have? Go to: Classify -> Choose -> Function -> SMO -> SMO option -> more (for explanation). Which kind of kernel function can you run in this version? Select use training set. Use the default set up, click on more options and …
https://www.cs.waikato.ac.nz/ml/weka/mooc/advanceddataminingwithweka/slides/Class3-AdvancedDataMiningWithWeka-2016.pdf
Weka’s most popular packages! Both packages implement them – Weka already has SMO (Data Mining with WekaLesson 4.5) – ... but LibSVM is more flexible; LibLINEAR can be much faster SVMs can be linear or non-linear: “kernel” functions SVMs can do classification or regression – Weka already has SMOreg for regression
https://www.slideshare.net/saipuji1/support-vector-machine-and-implementation-using-weka
May 11, 2017 · Support Vector Machine and Implementation using Weka 1. SVM, A Machine Learning Algorithm 2. Friendly Introduction to Machine Learning Learn from experience Learn from data Follow Instructions 3. What is Machine Learning? Machine Learning is the science of getting computers to act without being explicitly programmed.
https://stackoverflow.com/questions/40050498/support-vector-machine-on-r-and-weka
I'm not a machine learning person, so I'm guessing the default parameters are very different for these two methods. For example, e1071 has 0.01 as default epsilon and WEKA has 1.0E-12. I tried to read through the manuals and wanted to make all parameters identical, but a lot of parameters do not seem comparable to me. Thanks.
https://www.researchgate.net/post/How_to_implement_multi-class_classifier_SVM_in_WEKA
WEKA, by default, uses "SMO" algorithm that applies John Platt's sequential minimal optimization method in order to train a support vector classifier. In practice, as a rule of thumb, use a linear SVM, first. However, if the data are not linearly separable, you can then use the RBF kernel …
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