Optimization and evaluation of probabilistic-logic sequence models

Publikation: KonferencebidragPaperForskning

Abstract

Analysis of biological
sequence data demands more and more sophisticated and fine-grained models,
but these in turn introduce hard computational problems.
A class of probabilistic-logic models is considered, which increases the expressibility
from HMM's and SCFG's regular and context-free languages to,
in principle, Turing complete languages.
In general, such models are computationally far to complex for direct use, so
optimization by pruning and approximation are needed.
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The first steps are made towards a methodology for optimizing such models
by approximations
using auxiliary models for preprocessing or splitting them into submodels.
An evaluation method for approximating models is suggested
based on automatic generation of samples.
These models and evaluation processes are illustrated in the PRISM system
developed by other authors.

OriginalsprogEngelsk
Publikationsdato2008
Antal sider9
StatusUdgivet - 2008
BegivenhedStatistical and Relational Learning in Bioinformatics; Workshop of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Antwerpen, Belgien
Varighed: 15 sep. 200819 sep. 2008

Konference

KonferenceStatistical and Relational Learning in Bioinformatics; Workshop of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
LandBelgien
ByAntwerpen
Periode15/09/200819/09/2008

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