Fisher discriminant analysis with kernels
- S. Mika, Gunnar Rätsch, J. Weston, B. Scholkopf, K.R. Mullers
- Computer ScienceNeural Networks for Signal Processing IX…
- 23 August 1999
A non-linear classification technique based on Fisher's discriminant which allows the efficient computation of Fisher discriminant in feature space and large scale simulations demonstrate the competitiveness of this approach.
An introduction to kernel-based learning algorithms
- K. Müller, S. Mika, Gunnar Rätsch, K. Tsuda, B. Schölkopf
- Computer ScienceIEEE Trans. Neural Networks
- 1 March 2001
This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.…
Large Scale Multiple Kernel Learning
- S. Sonnenburg, Gunnar Rätsch, C. Schäfer, B. Schölkopf
- Computer ScienceJournal of machine learning research
- 1 December 2006
It is shown that the proposed multiple kernel learning algorithm can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations, and generalize the formulation and the method to a larger class of problems, including regression and one-class classification.
Kernel PCA and De-Noising in Feature Spaces
- S. Mika, B. Schölkopf, Alex Smola, K. Müller, Matthias Scholz, Gunnar Rätsch
- Computer ScienceNIPS
- 1 December 1998
This work presents ideas for finding approximate pre-images, focusing on Gaussian kernels, and shows experimental results using these pre- images in data reconstruction and de-noising on toy examples as well as on real world data.
Soft Margins for AdaBoost
- Gunnar Rätsch, T. Onoda, K. Müller
- Computer ScienceMachine-mediated learning
- 1 March 2001
It is found that ADABOOST asymptotically achieves a hard margin distribution, i.e. the algorithm concentrates its resources on a few hard-to-learn patterns that are interestingly very similar to Support Vectors.
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
- M. Weirauch, Ally Yang, T. Hughes
- BiologyCell
- 1 September 2014
Input space versus feature space in kernel-based methods
- B. Schölkopf, S. Mika, Alex Smola
- Computer ScienceIEEE Trans. Neural Networks
- 1 September 1999
The geometry of feature space is reviewed, and the connection between feature space and input space is discussed by dealing with the question of how one can, given some vector in feature space, find a preimage in input space.
The Molecular Taxonomy of Primary Prostate Cancer
- Adam Abeshouse, Jaeil Ahn, Douglas Voet
- Biology, MedicineCell
- 1 November 2015
Integrative Analysis of the Caenorhabditis elegans Genome by the modENCODE Project
- M. Gerstein, Z. Lu, R. Waterston
- BiologyScience
- 24 December 2010
These studies identified regions of the nematode and fly genomes that show highly occupied targets (or HOT) regions where DNA was bound by more than 15 of the transcription factors analyzed and the expression of related genes were characterized, providing insights into the organization, structure, and function of the two genomes.
Dual roles of the nuclear cap-binding complex and SERRATE in pre-mRNA splicing and microRNA processing in Arabidopsis thaliana
- S. Laubinger, Timo Sachsenberg, D. Weigel
- BiologyProceedings of the National Academy of Sciences
- 24 June 2008
The results uncover dual roles in splicing and miRNA processing that distinguish SE and the cap-binding complex from specialized mi RNA processing factors such as DCL1 and HYL1.
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