Home      
Staff       
Research  
Teaching  
About/Newsletter

  Algorithms  
  Courses     
  Data Sets   
  References 
  Theses     

 

ISPE PATCoP

Absalon

(CampusNet)
IFV IntraNet
LIFE phone.b.

SVM Starter

Introduction

Support vector machines and kernel methods have proven as powerful tools in numerous applications and have thus gained widespread popularity e.g. in machine learning and bioinformatics. They certainly do not violate the "no free lunch" theorem, i.e. they do not outperform every other technique in each and every pattern recognition application. But used properly they certainly constitute a very handy and powerful tool in the data analysis and pattern recognition toolkit.

This page is intended as an easy access point to this vast field with its own language and conventions. Thus, for the sake of simplicity and clarity it only covers a small and subjective collection of (hopefully) helpful links.

Tutorials/Textbooks

• A Tutorial on Support Vector Machines for Pattern Recognition by Chris Burges
• Support Vector Machines for Classification and Regression by Steve Gunn with accompanying Matlab toolbox (Caveat: Uses very inefficient optimization, not recommendable for use in real applications.)
• An Introduction to Support Vector Machines by Nello Cristianini and John Shawe-Taylor
• Learning with Kernels by Bernhard Schölkopf and Alex Smola
• Support Vector Learning Ph.D. by Bernhard Schölkopf
• Pattern Classification by R.O. Duda, P.E. Hart and D.G. Stork (not dedicated to SVMs but a "classic" textbook on pattern recognition)
• A flexible classification approach with optimal generalization performance: support vector machines by A.I. Belousov, S.A. Verzakov and J. von Frese

Websites

• kernel-machines.org
• support-vector-machines.org
• Journal of Machine Learning Research

Software

• OSU SVM Classifier Matlab Toolbox by J. Ma, Y. Zhao and S. Anhalt based on Chih-Jen Li's LIBSVM
• more recent LIBSVM Matlab interface
• Spider
• SVMlight by T. Joachims
• SVM in R
  Kernlab
  Interface to LIBSVM
• Software overview from kernel-machines.org
• PRTools (not dedicated to SVMs but a very comprehensive Matlab toolbox for pattern recognition)
• LinearSVM (a small handy Matlab function to use linear SVMs with the OSU SVM toolbox)

Reviews

• An introduction to Kernel-Based Learning Algorithms by K.-R. Müller et al.
• Statistical Pattern Recognition: A Review by A.K. Jain, R.P.W. Duin and J. Mao

Miscellanous Topics

• Feature selection by I. Guyon and A. Elisseeff
• One-Class SVM by D. Tax and R.P.W. Duin
• Support Vector Clustering by A. Ben-Hur et al.
• Kernel-PCA by B. Schölkopf, A. Smola and K.-R. Müller (see also Support Vector Learning)
• Eigenproblems (Kernel-PCA, CCA, PLS, Fisher Discriminant Analysis)
• Data integration

Chemometric Applications

• SVM Application List (applications in general)
• Automatic masking in multivariate image analysis using support vector machines by J. Jay Liu et al.
• Combination of support vector machines (SVM) and near-infrared (NIR) imaging spectroscopy for the detection of meat and bone meal (MBM) in compound feeds by J. A. Fernández Pierna et al.
• Support Vector Machine Regression in Chemometrics by K. Bennett et al.
• Comparing support vector machines to PLS for spectral regression applications by U. Thissen et al.
• Support Vector Machines for the Discrimination of Analytical Chemical Data: Application to the Determination of Tablet Production by Pyrolysis-gas Chromatography Mass Spectrometry by S. Zomer et al.
• Applicational aspects of support vector machines by A.I. Belousov, S.A. Verzakov and J. von Frese

Contact:

Associate Professor Jürgen von Frese
Department of Food Science
Food Technology
phone +45 3528 3254
e-mail jvf.at.kvl.dk