kernlab - An S4 package for kernel methods in R

Journal of Statistical Software

Volumn 11, Issue , 2004, Pages 1-20

  Karatzoglou, Alexandros ^a   Hornik, Kurt ^b   Smola, Alex ^c   Zeileis, Achim ^b  

^a VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

^b VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS   (Austria)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Clustering; Kernel methods; Quadratic programming; R; Ranking; S4; Support vector machines

Indexed keywords

EID: 11244352554     PISSN: 15487660     EISSN: None     Source Type: Journal    
DOI: 10.18637/jss.v011.i09     Document Type: Article

References (32)

1. Caputo B, Sim K, Furesjo F, Smola A (2002). "Appearance-based Object Recognition using SVMs: Which Kernel Should I Use?" Proc of NIPS workshop on Statistical methods for computational experiments in visual processing and computer vision, Whistler, 2002.
2. Chambers JM (1998). Programming with Data. Springer, New York. ISBN 0-387-98503-4.
3. Chang CC, Lin CJ (2001). "LIBSVM: A Library for Support Vector Machines." Software available at http://www.csie.ntu.edu.tw/∼cjlin/ libsvm.
4. Crammer K, Singer Y (2000). "On the Learnability and Design of Output Codes for Multiclass Prolems." Computational Learning Theory, pp. 35-46. URL http://www.cs.huji.ac.il/∼kobics/publications/mlj01.ps.gz.
5. Hastie T, Tibshirani R, Friedman JH (2001). The Elements of Statistical Learning. Springer.
6. Hsu CW, Lin CJ (2002a). "A Comparison of Methods for Multi-class Support Vector Machines." IEEE Transactions on Neural Networks, 13, 1045-1052. URL http://www.csie.ntu.edu.tw/∼cjlin/papers/multisvm.ps.gz.
7. Hsu CW, Lin CJ (2002b). "A Simple Decomposition Method for Support Vector Machines." Machine Learning, 46, 291-314. URL http://www.csie.ntu. edu.tw/∼cjlin/papers/decomp.ps.gz.
8. Joachims T (1999). "Making Large-scale SVM Learning Practical." In Advances in Kernel Methods - Support Vector Learning. URL http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_99a.ps.gz.
9. Knerr S, Personnaz L, Dreyfus G (1990). "Single-layer Learning Revisited: A Stepwise Procedure for Building and Training a Neural Network." J. Fogelman, editor, Neurocomputing: Algorithms, Architectures and Applications.
10. Kreßel U (1999). "Pairwise Classification and Support Vector Machines." B. Schölkopf, C. J. C. Burges, A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pp. 255-268.
11. Kuss M, Graepel T (2003). "The Geometry of Kernel Canonical Correlation Analysis." MPI-Technical Reports. URL http://www.kyb.mpg.de/ publication.html?publ=2233.
12. Leisch F, Dimitriadou E (2001). "mlbench-A Collection for Artificial and Real-world Machine Learning Benchmarking Problems." R package, Version 0.5-6. Available from http://CRAN. R-project.org.
13. Lin CJ, More JJ (1999). "Newton's Method for Large-scale Bound Constrained Problems." SIAM Journal on Optimization, 9, 1100-1127.
14. Lin HT, Lin CJ, Weng RC (2001). "A Note on Platt's Probabilistic Outputs for Support Vector Machines." Available at http://www.csie.ntu.edu. tw/∼cjlin/papers/plattprob.ps.
15. Meyer D, Leisch F, Hornik K (2003). "The Support Vector Machine under Test." Neurocomputing, 55, 169-186.
16. Ng AY, Jordan MI, Weiss Y (2001). "On Spectral Clustering: Analysis and an Algorithm." Neural Information Processing Symposium 2001. URL http://www.nips.cc/NIPS2001/papers/psgz/AA35.ps.gz.
17. Platt JC (2000). "Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods." Advances in Large Margin Classifiers, A. Smola, P. Bartlett, B. Schölkopf and D. Schuurmans, Eds. URL http://citeseer.nj.nec.com/platt99probabilistic.html.
18. Roever C, Raabe N, Luebke K, Ligges U (2004). "klaR - Classification and Visualization." R package, Version 0.3-3. Available from http://cran.R-project.org.
19. Schölkopf B, Platt J, Shawe-Taylor J, Smola AJ, Williamson RC (1999). "Estimating the Support of a High-Dimensonal Distribution." Microsoft Research, Redmond, WA, TR 87. URL http://research.microsoft.com/ research/pubs/view.aspx?msr_tr_ id=MSR-TR-99-87.
20. Schölkopf B, Smola A (2002). Learning with Kernels. MIT Press.
21. Scholkopf B, Smola A, Müller KR (1998). "Nonlinear Component Analysis as a Kernel Eigenvalue Problem." Neural Computation, 10, 1299-1319. URL http://mlg.anu.edu.au/∼smola/papers/SchSmoMul98.pdf.
22. Schölkopf B, Smola AJ, Williamson RC, Bartlett PL (2000). "New Support Vector Algorithms." Neural Computation, 12, 1207-1245. URL http://caliban.ingentaselect.com/vl=3338649/cl=47/nw=1/rpsv/cgi-bin/cgi?body= linker&reqidx=0899-7667(2000) 12:5L.1207.
23. Tax DMJ, Duin RPW (1999). "Support Vector Domain Description." Pattern Recognition Letters, 20, 1191-1199. URL http://www.ph.tn.tudelft.nl/ People/bob/papers/prl_99_svdd.pdf.
24. Tipping ME (2001). "Sparse Bayesian Learning and the Relevance Vector Machine." Journal of Machine Learning Research, 1, 211-244. URL http://www.jmlr.org/papers/volume1/tipping01a/tipping01a.pdf.
25. van der Putten P, de Ruiter M, van Someren M (2000). "CoIL Challenge 2000 Tasks and Results: Predicting and Explaining Caravan Policy Ownership." Coil Challenge 2000. URL http://www.liacs.nl/∼putten/ library/cc2000/.
26. Vanderbei R (1999). "LOQO: An Interior Point Code for Quadratic Programming." Optimization Methods and Software, 12, 251-484. URL http://www.sor.princeton.edu/∼rvdb/ps/loqo6.pdf.
27. Vapnik V (1995). The Nature of Statistical Learning Theory. Springer, NY.
28. Vapnik V (1998). Statistical Learning Theory. Wiley, New York.
29. Williams CKI, Rasmussen CE (1995). "Gaussian Processes for Regression." Advances in Neural Information Processing, 8. URL http://books.nips.cc/papers/files/nips08/0514.pdf.
30. Wright S (1999). "Modified Cholesky Factorizations in Interior-point Algorithms for Linear Programming." Journal in Optimization, 9, 1159-1191.
31. Wu TF, Lin CJ, Weng RC (2003). "Probability Estimates for Multi-class Classification by Pairwise Coupling." Advances in Neural Information Processing, 16. URL http://books. nips.cc/papers/files/nips16/ NIPS2003_0538.pdf.
32. Zhou D, Weston J, Gretton A, Bousquet O, Schölkopf B (2003). "Ranking on Data Manifolds." Advances in Neural Information Processing Systems, 16. URL http://www.kyb.mpg.de/publications/pdfs/pdf2334.pdf.