@MastersThesis{Son02,
  author =       {S\"oren Sonnenburg},
  title =        {New Methods for Splice Site Recognition},
  school =       {Humboldt University},
  year =         {2002},
  note =         {supervised by K.-R. M\"uller H.-D. Burkhard and G.~R{\"a}tsch},
  ps =           {http://sonnenburgs.de/soeren/publications/Son02.ps.gz},
  pdf =          {http://sonnenburgs.de/soeren/publications/Son02.pdf.gz},
  dataset = {http://www.fml.tuebingen.mpg.de/raetsch/projects/AnuSplice/},
  abstract = {
		  Modelling \emph{splice sites} is considered a difficult task, and as of this
		  writing, the procedure of splicing is still not well
		  understood. We combine successful \emph{discriminative learners} like
		  Support Vector Machines (SVM) and \emph{descriptive learners} like Hidden
		  Markov Models (HMMs) to separate true splice sites from decoys.
		  Recently developed kernel functions like the TOP- and Fisher Kernel (FK)
		  that are derived from generative models are used to combine SVMs and
		  HMMs. Furthermore, results for the well known Locality Improved Kernel
		  are presented and its connection to the FK, derived from a special HMM
		  is shown.  Finally we provide an experimental analysis of splice sites
		  and investigate the classification performance using a variety of
		  learning machines.
  }
}