@inbook{gehler09introduction,
  title = {An introduction to Kernel Learning Algorithms},
  booktitle = {Kernel Methods for Remote Sensing Data Analysis},
  abstract = {Kernel learning algorithms are currently becoming a standard tool in the area of machine learning and pattern recognition.
  In this chapter we review the fundamental theory of kernel learning. As the basic building block we introduce the kernel function,
  which provides an elegant and general way to compare possibly very complex objects. We then review the concept
  of a reproducing kernel Hilbert space and state the representer theorem. Finally we give an overview of the most
  prominent algorithms, which are support vector classification and regression, Gaussian Processes and kernel principal analysis.
  With multiple kernel learning and structured output prediction we also introduce some more recent advancements in the field.},
  pages = {25--48},
  chapter = {2},
  editors = {Gustavo Camps-Valls and Lorenzo Bruzzone},
  publisher = {Wiley},
  address = {New York, NY, USA},
  year = {2009},
  slug = {gehler09introduction},
  author = {Gehler, Peter and Sch{\"o}lkopf, Bernhard},
  url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470722118.html}
}