kernel references

[Aronszajn1950Theory] N. Aronszajn. Theory of reproducing kernels. Trans. Am. Math. Soc., 68:337 - 404, 1950. [ bib | .pdf ]
[Saitoh1988Theory] S. Saitoh. Theory of reproducing Kernels and its applications. Longman Scientific & Technical, Harlow, UK, 1988. [ bib ]
[Boser1992training] B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the 5th annual ACM workshop on Computational Learning Theory, pages 144-152, New York, NY, USA, 1992. ACM Press. [ bib | .ps.Z | .pdf ]
[Vapnik1998Statistical] V. N. Vapnik. Statistical Learning Theory. Wiley, New-York, 1998. [ bib ]
[Mukherjee1998Support] S. Mukherjee, P. Tamayo, J. P. Mesirov, D. Slonim, A. Verri, and T. Poggio. Support vector machine classification of microarray data. Technical Report 182, C.B.L.C., 1998. A.I. Memo 1677. [ bib | .html | .pdf ]
[Burges1998Tutorial] C. J. C. Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Min. Knowl. Discov., 2(2):121-167, 1998. [ bib | .ps.gz | .pdf ]
[Schoelkopf1999Kernel] B. Schölkopf, A.J. Smola, and K.-R. Müller. Kernel principal component analysis. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 327-352. MIT Press, 1999. [ bib | .pdf ]
[Platt1999Fast] J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 185-208. MIT Press, Cambridge, MA, USA, 1999. [ bib ]
[Mika1999Fisher] S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K.R. Müller. Fisher discriminant analysis with kernels. In Y.-H. Hu, J. Larsen, E. Wilson, and S. Douglas, editors, Neural Networks for Signal Processing IX, pages 41-48. IEEE, 1999. [ bib | .ps | .pdf ]
[Jaakkola1999Probabilistic] T. S. Jaakkola and D. Haussler. Probabilistic kernel regression models. In Proceedings of the 1999 Conference on AI and Statistics. Morgan Kaufmann, 1999. [ bib | .ps.gz | .pdf ]
[Jaakkola1999Exploiting] T. S. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In Proc. of Tenth Conference on Advances in Neural Information Processing Systems, 1999. [ bib | .ps | .pdf ]
[Haussler1999Convolution] D. Haussler. Convolution Kernels on Discrete Structures. Technical Report UCSC-CRL-99-10, UC Santa Cruz, 1999. [ bib | .pdf | Abstract ]
[Amari1999Improving] S.-I. Amari and S. Wu. Improving support vector machine classifiers by modifying kernel functions. Neural Networks, 12(6):783-789, Jul 1999. [ bib | .ps | .pdf | Abstract ]
[Watkins2000Dynamic] C. Watkins. Dynamic alignment kernels. In A.J. Smola, P.L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 39-50. MIT Press, Cambridge, MA, 2000. [ bib | .ps.gz | .pdf ]
[Scholkopf2000Support] B. Schölkopf, R. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt. Support Vector Method for Novelty Detection. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Adv. Neural Inform. Process. Syst., volume 12, pages 582-588. MIT Press, 2000. [ bib | .html | .pdf ]
[Lodhi2000Text] H. Lodhi, J. Shawe-Taylor, N. Cristianini, and C. J. C. H. Watkins. Text Classification using String Kernels. In Adv. Neural Inform. Process. Syst., pages 563-569, 2000. [ bib | .ps.gz | .pdf ]
[Lai2000Kernel] P.L. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. Int. J. Neural Syst., 10(5):365-377, 2000. [ bib | .html | .pdf ]
[Jaakkola2000Discriminative] T. Jaakkola, M. Diekhans, and D. Haussler. A Discriminative Framework for Detecting Remote Protein Homologies. J. Comput. Biol., 7(1,2):95-114, 2000. [ bib | .ps | .pdf ]
[Cristianini2000introduction] N. Cristianini and J. Shawe-Taylor. An introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000. [ bib | http ]
[Pavlidis2001Gene] P. Pavlidis, J. Weston, J. Cai, and W.N. Grundy. Gene functional classification from heterogeneous data. In Proceedings of the Fifth Annual International Conference on Computational Biology, pages 249-255, 2001. [ bib | .pdf | .pdf ]
[Pavlidis2001Promoter] P. Pavlidis, T. S. Furey, M. Liberto, D. Haussler, and W. N. Grundy. Promoter Region-Based Classification of Genes. In Pacific Symposium on Biocomputing, pages 139-150, 2001. [ bib | .pdf | .pdf ]
[Ding2001Multi-class] C.H.Q. Ding and I. Dubchak. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17:349-358, 2001. [ bib | .pdf | .pdf | Abstract ]
[Bock2001Predicting] J. R. Bock and D. A. Gough. Predicting protein-protein interactions from primary structure. Bioinformatics, 17(5):455-460, 2001. [ bib | .pdf | .pdf ]
[Ben-Hur2001Support] A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik. Support Vector Clustering. J. Mach. Learn. Res., 2:125-137, 2001. [ bib | .pdf | .pdf ]
[Hua2001Novel] S. Hua and Z. Sun. A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach. J. Mol. Biol., 308(2):397-407, April 2001. [ bib | DOI | .pdf ]
[Zavaljevski2002Support] N. Zavaljevski, F.J. Stevens, and J. Reifman. Support vector machines with selective kernel scaling for protein classification and identification of key amino acid positions. Bioinformatics, 18(5):689-696, 2002. [ bib | http | .pdf | Abstract ]
[Vinokourov2002Finding] A. Vinokourov, J. Shawe-Taylor, and N. Cristianini. Finding Language-Independent Semantic Representation of Text using Kernel Canonical Correlation Analysis. Technical report, Neurocolt, 2002. NeuroCOLT Technical Report NC-TR-02-119. [ bib | .html | .ps.gz ]
[Vert2002tree] J.-P. Vert. A tree kernel to analyze phylogenetic profiles. Bioinformatics, 18:S276-S284, 2002. [ bib | .html | .pdf ]
[Vert2002Support] J.-P. Vert. Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings. In R. B. Altman, A. K. Dunker, L. Hunter, K. Lauerdale, and T. E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing 2002, pages 649-660. World Scientific, 2002. [ bib | .pdf | .pdf ]
[Stapley2002Predicting] B.J. Stapley, L.A. Kelley, and M.J. Sternberg. Predicting the sub-cellular location of proteins from text using support vector machines. In Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Kevin Lauerdale, and Teri E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing 2002, pages 374-385. World Scientific, 2002. [ bib | .pdf | .pdf | Abstract ]
[Schoelkopf2002Kernel] B. Schölkopf, J. Weston, E. Eskin, C. Leslie, and W.S. Noble. A Kernel Approach for Learning from Almost Orthogonal Patterns. In Proceedings of ECML 2002, 2002. [ bib | .pdf | .pdf ]
[Scholkopf2002Learning] B. Schölkopf and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, 2002. [ bib | http ]
[Patterson2002Pre-mRNA] D.J. Patterson, K. Yasuhara, and W.L. Ruzzo. Pre-mRNA secondary structure prediction aids splice site prediction. In Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Kevin Lauerdale, and Teri E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing 2002, pages 223-234. World Scientific, 2002. [ bib | .pdf | .pdf | Abstract ]
[Lodhi2002Text] H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C.je n'ai pas vraiment d'éléments de réponse. Watkins. Text classification using string kernels. J. Mach. Learn. Res., 2:419-444, 2002. [ bib | .html | .pdf ]
[Liao2002Combining] L. Liao and W. S. Noble. Combining pairwise sequence similarity and support vector machines for remote protein homology detection. In Proceedings of the Sixth International Conference on Computational Molecular Biology, 2002. [ bib | .html | .pdf ]
[Leslie2002spectrum] C. Leslie, E. Eskin, and W.S. Noble. The spectrum kernel: a string kernel for SVM protein classification. In Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Kevin Lauerdale, and Teri E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing 2002, pages 564-575, Singapore, 2002. World Scientific. [ bib | .pdf ]
[Kondor2002Diffusion] R. I. Kondor and J. Lafferty. Diffusion kernels on graphs and other discrete input. In Proceedings of the Nineteenth International Conference on Machine Learning, pages 315-322, San Francisco, CA, USA, 2002. Morgan Kaufmann Publishers Inc. [ bib | .pdf ]
[Karchin2002Classifying] R. Karchin, K. Karplus, and D. Haussler. Classifying G-protein coupled receptors with support vector machines. Bioinformatics, 18:147-159, 2002. [ bib | http | .pdf | Abstract ]
[Kandola2002On] J. Kandola, J. Shawe-Taylor, and N. Cristianini. On the application of diffusion kernel to text data. Technical report, Neurocolt, 2002. NeuroCOLT Technical Report NC-TR-02-122. [ bib | .html | .ps.gz ]
[Guyon2002Gene] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Mach. Learn., 46(1/3):389-422, Jan 2002. [ bib | .pdf | .pdf | Abstract ]
[Bao2002Identifying] L. Bao and Z. Sun. Identifying genes related to drug anticancer mechanisms using support vector machine. FEBS Lett., 521:109-114, 2002. [ bib | .html | .pdf | Abstract ]
[Bach2002Kernel] F.R. Bach and M.I. Jordan. Kernel independent component analysis. J. Mach. Learn. Res., 3:1-48, 2002. [ bib | .html | .pdf ]
[Andrews2002Multiple] S. Andrews, T. Hofmann, and I. Tsochantaridis. Multiple Instance Learning with Generalized Support Vector Machines. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pages 943-944. American Association for Artificial Intelligence, 2002. [ bib ]
[Wolf2003Learning] L. Wolf and A. Shashua. Learning over Sets using Kernel Principal Angles. J. Mach. Learn. Res., 4:913-931, 2003. [ bib | .html ]
[Ramon2003Expressivity] J. Ramon and T. Gärtner. Expressivity versus efficiency of graph kernels. In T. Washio and L. De Raedt, editors, Proceedings of the First International Workshop on Mining Graphs, Trees and Sequences, pages 65-74, 2003. [ bib ]
[Leslie2003Mismatch] C. Leslie, E. Eskin, J. Weston, and W.S. Noble. Mismatch String Kernels for SVM Protein Classification. In Suzanna Becker, Sebastian Thrun, and Klaus Obermayer, editors, Advances in Neural Information Processing Systems 15. MIT Press, 2003. [ bib | .pdf | .pdf ]
[Gartner2003Survey] T. Gärtner. A Survey of Kernels for Structured Data. SIGKDD Explor. Newsl., 5(1):49-58, 2003. [ bib | DOI ]
[Tsuda2004Learning] K. Tsuda and W.S. Noble. Learning kernels from biological networks by maximizing entropy. Bioinformatics, 20:i326-i333, 2004. [ bib | DOI | http | .pdf | Abstract ]
[Lanckriet2004Learning] G.R.G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M.I. Jordan. Learning the kernel matrix with semidefinite programming. J. Mach. Learn. Res., 5:27-72, 2004. [ bib | .html | .pdf ]
[Jebara2004Probability] T. Jebara, R. Kondor, and A. Howard. Probability Product Kernels. J. Mach. Learn. Res., 5:819-844, 2004. [ bib | .html ]
[Horvath2004Cyclic] T. Horváth, T. Gärtner, and S. Wrobel. Cyclic pattern kernels for predictive graph mining. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 158-167, New York, NY, USA, 2004. ACM Press. [ bib | DOI ]
[Cuturi2005Semigroupa] M. Cuturi, K. Fukumizu, and J.-P. Vert. Semigroup kernels on measures. J. Mach. Learn. Res., 6:1169-1198, 2005. [ bib | .html | .pdf ]
[Borgwardt2005Shortest-Path] Karsten M. Borgwardt and Hans-Peter Kriegel. Shortest-path kernels on graphs. In ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining, pages 74-81, Washington, DC, USA, 2005. IEEE Computer Society. [ bib | DOI | .pdf ]
[Mahe2006pharmacophorea] P. Mahé, L. Ralaivola, V. Stoven, and J.-P. Vert. The pharmacophore kernel for virtual screening with support vector machines. Technical Report Technical Report HAL:ccsd-00020066, Ecole des Mines de Paris, march 2006. [ bib | http ]
[Mahe2006Graph] P. Mahé and J.-P. Vert. Graph kernels based on tree patterns for molecules. Technical Report ccsd-00095488, HAL, September 2006. [ bib | http ]
[Kim2008Robust] J. S. Kim and C. Scott. Robust kernel density estimation. In Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing ICASSP 2008, pages 3381-3384, 2008. [ bib | DOI | http | .pdf ]

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