Optimization and evaluation of probabilistic-logic sequence models

Research output: Contribution to conferencePaperResearch

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.
%
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.


Original languageEnglish
Publication date2008
Number of pages9
Publication statusPublished - 2008
EventStatistical and Relational Learning in Bioinformatics; Workshop of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Antwerpen, Belgium
Duration: 15 Sep 200819 Sep 2008

Conference

ConferenceStatistical and Relational Learning in Bioinformatics; Workshop of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
CountryBelgium
CityAntwerpen
Period15/09/200819/09/2008

Cite this

Christiansen, H., & Lassen, O. T. (2008). Optimization and evaluation of probabilistic-logic sequence models. Paper presented at Statistical and Relational Learning in Bioinformatics; Workshop of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Antwerpen, Belgium.