Empirische Inferenz
Conference Paper
2009
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
PDF Web
Empirische Inferenz
Research Group Leader Computational Epigenomics University of Dundee and Universität Tübingen
Empirische Inferenz
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 adaptation methods can help improve classification performance.
Author(s): | Schweikert, G. and Widmer, C. and Schölkopf, B. and Rätsch, G. |
Links: | |
Book Title: | Advances in neural information processing systems 21 |
Journal: | Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008 |
Pages: | 1433-1440 |
Year: | 2009 |
Month: | June |
Day: | 0 |
Editors: | D Koller and D Schuurmans and Y Bengio and L Bottou |
Publisher: | Curran |
Bibtex Type: | Conference Paper (inproceedings) |
Address: | Red Hook, NY, USA |
Event Name: | 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008) |
Event Place: | Vancouver, BC, Canada |
Digital: | 0 |
Electronic Archiving: | grant_archive |
Institution: | NIPS 2008 |
ISBN: | 978-1-605-60949-2 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
BibTex
@inproceedings{5401, title = {An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis}, journal = {Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008}, booktitle = {Advances in neural information processing systems 21}, 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 adaptation methods can help improve classification performance.}, pages = {1433-1440}, editors = {D Koller and D Schuurmans and Y Bengio and L Bottou}, publisher = {Curran}, organization = {Max-Planck-Gesellschaft}, institution = {NIPS 2008}, school = {Biologische Kybernetik}, address = {Red Hook, NY, USA}, month = jun, year = {2009}, slug = {5401}, author = {Schweikert, G. and Widmer, C. and Sch{\"o}lkopf, B. and R{\"a}tsch, G.}, month_numeric = {6} }