Gebruikersprofielen voor author:"Rätsch Gunnar"

Gunnar Rätsch

Professor, ETH Zürich
Geverifieerd e-mailadres voor inf.ethz.ch
Geciteerd door 40796

[PDF][PDF] Large scale multiple kernel learning

S Sonnenburg, G Rätsch, C Schäfer… - The Journal of Machine …, 2006 - jmlr.org
While classical kernel-based learning algorithms are based on a single kernel, in practice it
is often desirable to use multiple kernels. Lanckriet et al.(2004) considered conic
combinations of kernel matrices for classification, leading to a convex quadratically …

Soft margins for AdaBoost

G Rätsch, T Onoda, KR Müller - Machine learning, 2001 - Springer
Recently ensemble methods like ADABOOST have been applied successfully in many
problems, while seemingly defying the problems of overfitting. ADABOOST rarely overfits in
the low noise regime, however, we show that it clearly does so for higher noise levels …

[PDF][PDF] Kernel PCA and De-noising in feature spaces.

S Mika, B Schölkopf, AJ Smola, KR Müller, M Scholz… - NIPS, 1998 - academia.edu
Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for
classification algorithms. But it can also be considered as a natural generalization of linear
principal component analysis. This gives rise to the question how to use nonlinear features …

Predicting time series with support vector machines

KR Müller, AJ Smola, G Rätsch, B Schölkopf… - … Conference on Artificial …, 1997 - Springer
Abstract Support Vector Machines are used for time series prediction and compared to radial
basis function networks. We make use of two different cost functions for Support Vectors:
training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to …

Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project

MB Gerstein, ZJ Lu, EL Van Nostrand, C Cheng… - …, 2010 - science.sciencemag.org
We systematically generated large-scale data sets to improve genome annotation for the
nematode Caenorhabditis elegans, a key model organism. These data sets include
transcriptome profiling across a developmental time course, genome-wide identification of …

Common sequence polymorphisms shaping genetic diversity in Arabidopsis thaliana

RM Clark, G Schweikert, C Toomajian… - …, 2007 - science.sciencemag.org
The genomes of individuals from the same species vary in sequence as a result of different
evolutionary processes. To examine the patterns of, and the forces shaping, sequence
variation in Arabidopsis thaliana, we performed high-density array resequencing of 20 …

[HTML][HTML] Multiple reference genomes and transcriptomes for Arabidopsis thaliana

X Gan, O Stegle, J Behr, JG Steffen, P Drewe… - Nature, 2011 - nature.com
Genetic differences between Arabidopsis thaliana accessions underlie the plant's extensive
phenotypic variation, and until now these have been interpreted largely in the context of the
annotated reference accession Col-0. Here we report the sequencing, assembly and …

[HTML][HTML] Support vector machines and kernels for computational biology

A Ben-Hur, CS Ong, S Sonnenburg… - PLoS Comput …, 2008 - journals.plos.org
The increasing wealth of biological data coming from a large variety of platforms and the
continued development of new high-throughput methods for probing biological systems
require increasingly more sophisticated computational approaches. Putting all these data in …

An introduction to boosting and leveraging

R Meir, G Rätsch - Advanced lectures on machine learning, 2003 - Springer
We provide an introduction to theoretical and practical aspects of Boosting and Ensemble
learning, providing a useful reference for researchers in the field of Boosting as well as for
those seeking to enter this fascinating area of research. We begin with a short background …

Engineering support vector machine kernels that recognize translation initiation sites

A Zien, G Rätsch, S Mika, B Schölkopf… - …, 2000 - academic.oup.com
Motivation: In order to extract protein sequences from nucleotide sequences, it is an
important step to recognize points at which regions start that code for proteins. These points
are called translation initiation sites (TIS). Results: The task of finding TIS can be modeled as …