PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
G. Schweikert, C. Widmer, B. Schölkopf and G. Rätsch
In: The Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), 8-11 Dec 2008, Vancouver, Canada.

Abstract

We study the problem of domain transfer for a supervised classification task in mRNA splicing. We consider a number of recent domain transfer methods from machine learning, including some that are novel, and evaluate them on genomic sequence data from model organisms of varying evolutionary distance. We find that in cases where the organisms are not closely related, the use of domain adap- tation methods can help improve classification performance.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
ID Code:4350
Deposited By:Bernhard Schölkopf
Deposited On:13 March 2009