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

Online CoursesEdit

Video LecturesEdit

Lecture NotesEdit

Books and Book Chapters Edit

  • 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 Edit

SoftwareEdit

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

See also Edit

Other Resources Edit