A practical guide to support vector classification-英文文献.pdfVIP

A Practical Guide to Support Vector Classification Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin Department of Computer Science National Taiwan University, Taipei 106, Taiwan http ://www .csie .ntu .edu .tw/~cjlin Initial version: 2003 Last updated: April 15, 2010 Abstract The support vector machine (SVM) is a popular classification technique. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure which usually gives reasonable results. 1 Introduction SVMs (Support Vector Machines) are a useful technique for data classification. Al- though SVM is considered easier to use than Neural Networks, users not familiar with it often get unsatisfactory results at first. Here we outline a “cookbook” approach which usually gives reasonable results. Note that this guide is not for SVM researchers nor do we guarantee you will achieve the highest accuracy. Also, we do not intend to solve challenging or di- cult problems. Our purpose is to give SVM novices a recipe for rapidly obtaining acceptable results. Although users do not need to understand the underlying theory behind SVM, we briefly introduce the basics necessary for explaining our procedure. A classification task usually involves separating data into training and testing sets. Each instance in the training set contains one “target value” (i.e. the class labels) and several “attributes” (i.e. the features or observed variables). The goal of SVM is to produce a model (based on the training data) which predicts t