Robust learning of inhomogeneous PMMs

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http://hdl.handle.net/10138/153190

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Eggeling , R , Roos , T , Myllymäki , P & Grosse , I 2014 , Robust learning of inhomogeneous PMMs . in S Kaski & J Corander (eds) , Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS-2014) . JMLR: Workshop and Conference Proceedings , vol. 33 , pp. 229-237 , International Conference on Artificial Intelligence and Statistics , Reykjavik , Iceland , 22/04/2014 . < http://jmlr.org/proceedings/papers/v33/ >

Title: Robust learning of inhomogeneous PMMs
Author: Eggeling, Ralf; Roos, Teemu; Myllymäki, Petri; Grosse, Ivo
Editor: Kaski, Samuel; Corander, Jukka
Contributor: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
Date: 2014-04
Language: eng
Number of pages: 9
Belongs to series: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS-2014)
Belongs to series: JMLR: Workshop and Conference Proceedings
URI: http://hdl.handle.net/10138/153190
Abstract: Inhomogeneous parsimonious Markov models have recently been introduced for modeling symbolic sequences, with a main application being DNA sequence analysis. Structure and parameter learning of these models has been proposed using a Bayesian approach, which entails the practically challenging choice of the prior distribution. Cross validation is a possible way of tuning the prior hyperparameters towards a specific task such as prediction or classification, but it is overly time-consuming. On this account, robust learning methods, which do not require explicit prior specification and – in the absence of prior knowledge – no hyperparameter tuning, are of interest. In this work, we empirically investigate the performance of robust alternatives for structure and parameter learning that extend the practical applicability of parsimonious Markov models to more complex settings than before.
Subject: 113 Computer and information sciences
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