One of the most advance supervised learning method is called Support Vector Machine or SVM in short. SVM was originally developed by Vapnik and Cortes and colleagues in 1992 based on the groundwork from Vapnik & Chervonenkis’ statistical learning theory in 1960s. SVM has been successfully applied in many applications including handwritten recognition, time-series prediction, speech Recognition, database marketing, protein sequence problem, breast cancer diagnosis and many more. If you have ever read any other books on SVM, you will know how notoriously difficult it is to understand SVM. Fortunately, there is a tutorial by Dr. Kardi Teknomo that will give you a very gentle introduction to SVM by giving simple step by step numerical solution using Microsoft Excel. Yes, to build your own SVM model, you do not need any special software aside from Microsoft Excel. You can save thousands of dollar cost of the software and build your own model to suit your purpose. You will learn about how you can train the SVM model, how to evaluate and use the SVM model to predict the classification. You will also learn by complete numerical example about how to handle linearly non-separable cases using slack variables and kernel tricks. By reading and do the practice of the numerical examples of this tutorial to the end, at least you will be able to ready to read other more advanced SVM books. In this tutorial, you will learn step by step numerical solution on how to implement SVM learning and optimization and prediction of both linearly separable and linearly non-separable cases and using only Microsoft Excel without any macro programming. You can enjoy reading the sample of the first two sections of Kardi Teknomo’s SVM tutorial from his web site. You can see that the difficult topics have been transformed into systematic and very simple explanations. If you purchase the e-book of Kardi Teknomo’s SVM tutorial, you will learn much more topics than the free tutorial in this web site. You will understand how to perform Training on Support Vector Machines, Evaluating SVM training and Predicting with SVM by a complete numerical examples for Linearly Separable Cases as well as for Two Linearly Non-Separable Cases. You will learn How to use Slack Variables, Kernel Trick, Numerical Examples of Kernel Transformation, SVM for Multi classes with examples, including the strengths and weaknesses of SVM and discussion on step by step on solving dual problem of SVM. What is more, the e-book also comes with many spreadsheet companion files that include your own practice worksheets and the complete solutions. The complete solution include SVM training, evaluation and prediction. This e-book SVM tutorial and its companion files are designed to greatly enhance your understanding of SVM in very gentle and fast to learn. With this e-book as your guide, learning SVM becomes easy steps and enjoyable practice moment. You can find more interesting tutorial about SVM tutorial from people.revoledu/kardi/tutorial/SVM/purchase.html. Visit the web site today. Many more fascinating free tutorials are waiting for you to learn.
Posted on: Tue, 06 Jan 2015 01:54:01 +0000
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