title: Toward multistrategy parallel and distributed learning in sequence analysis.
abstract: Machine learning techniques have been shown to be effective in sequence analysis tasks. However, current learning algorithms, which are typically serial main-memory-based, are not capable of handling the vast amounts of information being generated by the Human Genome Project. The multistrategy parallel learning approach presented in this paper is an attempt to scale existing learning algorithms. Learning speed is improved through running multiple learning processes in parallel and prediction accuracy is improved through multiple learners. Our approaches are independent of the learning algorithms used. This paper focuses on one of the MSPL approaches and preliminary empirical results that we present are encouraging.
authors.affiliation: Department of Computer Science, Columbia University, New York, NY 10027, USA.
authors.forename: P K
authors.initials: PK
authors.lastname: Chan
authors.forename: S J
authors.initials: SJ
authors.lastname: Stolfo
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D017433:Protein Structure, Secondary; D012313:RNA / Q000737:chemistry*; D012326:RNA Splicing; D017421:Sequence Analysis / Q000379:methods*
chemical_list: D012313:RNA