Kernel Method

This page contains resources about Kernel Methods, Kernel Machines and Reproducing kernel Hilbert spaces.

Subfields and Concepts

 * Gaussian Radial-basis function (RBF) kernel
 * Graph kernels
 * Weisfeiler-Lehman kernel

Video Lectures

 * Statistical Learning Theory and Applications by Tomaso Poggio and Lorenzo Rosasco

Lecture Notes

 * Reproducing kernel Hilbert spaces in Machine Learning by Arthur Gretton
 * What is an RKHS? by Dino Sejdinovic and Arthur Gretton

Books and Book Chapters

 * Scholkopf, B., & Smola, A. J. (2001). Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press.
 * Taylor,J. S. & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press.
 * Bishop, C. M. (2006). "Chapter 6: Kernel Methods". Pattern Recognition and Machine Learning. Springer.
 * Smola, A., & Vishwanathan, S. V. N. (2008). "Chapter 6: Kernels and Function Spaces". Introduction to Machine Learning. Cambridge University Press.
 * Alpaydin, E. (2010). "Chapter 13: Kernel Machines". Introduction to machine learning. MIT Press.
 * Liu, W., Principe, J. C., & Haykin, S. (2011). Kernel adaptive filtering: a comprehensive introduction (Vol. 57). John Wiley & Sons.
 * Barber, D. (2012). "Chapter 17: Linear Models". Bayesian Reasoning and Machine Learning. Cambridge University Press.
 * Murphy, K. P. (2012). "Chapter 16: Kernel Methods". Machine Learning: A Probabilistic Perspective. MIT Press.
 * Shalev-Shwartz, S., & Ben-David, S. (2014). "Chapter 14: Kernels". Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.
 * Suykens, J. A., Signoretto, M., & Argyriou, A. (Eds.). (2014). Regularization, optimization, kernels, and support vector machines. CRC Press.
 * Theodoridis, S. (2015). "Chapter 11: Learning in Reproducing Kernel Hilbert Spaces". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.

Scholarly Articles

 * Shervashidze, N., Schweitzer, P., Leeuwen, E. J. V., Mehlhorn, K., & Borgwardt, K. M. (2011). Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, 12(Sep), 2539-2561.
 * Shervashidze, N., & Borgwardt, K. M. (2009). Fast subtree kernels on graphs. In Advances in Neural Information Processing Systems (pp. 1660-1668).
 * Vishwanathan, S. V. N., Smola, A. J., & Vidal, R. (2007). Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. International Journal of Computer Vision, 73(1), 95-119.

Tutorials

 * The Weisfeiler-Lehman Kernel by Karsten Borgwardt and Nino Shervashidze

Software

 * Shogun - C++ toolbox (for Kernel Machines) that offers interfaces for MATLAB, Octave, Python, R, Java, Lua, Ruby and C#

Other Resources

 * Hilbert Space Methods for Statistics and Probability - Notebook
 * Kernel-Machines.Org