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https://www.futurelearn.com/courses/data-mining-with-weka/3/steps/290151
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
Support vector machines. Introduction to WEKA Morten Nielsen ([email protected]) Olivier Taboureau ([email protected]) For the first (and only) time in the course you shall not be developing your own code, but rather use a program package called WEKA.
https://www.tutorialspoint.com/weka/weka_tutorial.pdf
Weka i About the Tutorial Weka is a comprehensive software that lets you to preprocess the big data, apply different machine learning algorithms on big data and compare various outputs. This software makes it easy to work with big data and train a machine using machine learning algorithms.
https://machinecreek.com/weka-part-3/
In Part 2 Weka’s IBk algorithm was tuned and used for predicting the volume of new product sales. Here we use Weka’s Support Vector Machine. SVM: Support Vector Machine. Instead of a neighborhood deciding similarity, the support vector machine algorithm seeks to find the widest possible margin that separates classes.
https://weka.8497.n7.nabble.com/Using-Support-Vector-Machine-td17430.html
Using Support Vector Machine. Hi, I am working in web usage mining area and planing to use SVM implemntation in WEKA. I found implementaion of SVM in Weka.I believe WEKA implementation of …
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