2003 APracticalGuideToSVClassification

(Hsu et al., 2003) ⇒ Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin. (2003). “A Practical Guide to Support Vector Classification.” Technical report, Department of Computer Science and Information Engineering, [[National Taiwan University]], Taipei, 2003.

Subject Headings: LIBSVM System.

Notes

Cited BY

2004

(Hastie et al., 2004) ⇒ Trevor Hastie, Saharon Rosset, Robert Tibshirani, and Ji Zhu. (2004). “The Entire Regularization Path for the Support Vector Machine.” In: The Journal of Machine Learning Research, 5.
- It seems that the regularization parameter C (or l) is often regarded as a genuine “nuisance” in the community of SVM users. Software packages, such as the widely used SVM^light (Joachims, 1999), provide default settings for C, which are then used without much further exploration. A recent introductory document (Hsu et al., 2003) supporting the LIBSVM package does encourage grid search for C.

Quotes

Abstract

Support vector machine (SVM) is a popular technique for classification. 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

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1. Introduction

A classification task usually involves with training and testing data which consist of some data instances. Each instance in the training set contains one “target value” (class labels) and several “attributes” (features). The goal of SVM is to produce a model which predicts target value of data instances in the testing set which are given only the attributes.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 APracticalGuideToSVClassification	Chih-Jen Lin Chih-Wei Hsu Chih-Chung Chang			A Practical Guide to Support Vector Classification			http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf			2003