2008
NIPS
NeurIPS 2008
An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
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.
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Interdisciplinary Bridge
— Healthcare & Medicine and Machine Learning
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Trend Setter
— Domain Adaptation
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Keyword Pioneer
— genomic sequence analysis
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Hot Topic Early Bird
— transfer learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Topics
Machine Learning > Application Areas > Domain Adaptation
Healthcare & Medicine > Research > Bioinformatics
Machine Learning > Learning Types > Transfer Learning
Machine Learning > Learning Types > Domain Adaptation
Interdisciplinary > Science > Bioinformatics
Machine Learning > Learning Paradigms > Domain Adaptation