Robust learning of inhomogeneous PMMs

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Pysyväisosoite

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

Lähdeviite

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/ >

Julkaisun nimi: Robust learning of inhomogeneous PMMs
Tekijä: Eggeling, Ralf; Roos, Teemu; Myllymäki, Petri; Grosse, Ivo
Toimittaja(t): Kaski, Samuel; Corander, Jukka
Muu tekijä: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
Päiväys: 2014-04
Kieli: eng
Sivumäärä: 9
Kuuluu julkaisusarjaan: Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS-2014)
Kuuluu julkaisusarjaan: JMLR: Workshop and Conference Proceedings
URI: http://hdl.handle.net/10138/153190
Tiivistelmä: 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.
Avainsanat: 113 Computer and information sciences
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