Název: Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions
Autoři: Plajner, Martin ; Vomlel, Jiří
Typ dokumentu: Příspěvky z konference
Konference/Akce: Workshop on Uncertainty Processing (WUPES’18), Třeboň (CZ), 20180606
Rok: 2018
Jazyk: eng
Abstrakt: Learning parameters of a probabilistic model is a necessary step in most machine learning modeling tasks. When the model is complex and data volume is small the learning process may fail to provide good results. In this paper we present a method to improve learning results for small data sets by using additional information about the modelled system. This additional information is represented by monotonicity conditions which are restrictions on parameters of the model. Monotonicity simplifies the learning process and also these conditions are often required by the user of the system to hold. \n\nIn this paper we present a generalization of the previously used algorithm for parameter learning of Bayesian Networks under monotonicity conditions. This generalization allows both parents and children in the network to have multiple states. The algorithm is described in detail as well as monotonicity conditions are.\n\nThe presented algorithm is tested on two different data sets. Models are trained on differently sized data subsamples with the proposed method and the general EM algorithm. Learned models are then compared by their ability to fit data. We present empirical results showing the benefit of monotonicity conditions. The difference is especially significant when working with small data samples. The proposed method outperforms the EM algorithm for small sets and provides comparable results for larger sets.
Klíčová slova: Bayesian networks; Learning model parameters; monotonicity condition
Číslo projektu: GA16-12010S (CEP), SGS17/198/OHK4/3T/14 (CEP)
Zdrojový dokument: Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18), ISBN 978-80-7378-361-7

Instituce: Ústav teorie informace a automatizace AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný na externích webových stránkách.
Externí umístění souboru: http://library.utia.cas.cz/separaty/2018/MTR/plajner-0490309.pdf
Původní záznam: http://hdl.handle.net/11104/0284592

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-375672


Záznam je zařazen do těchto sbírek:
Věda a výzkum > AV ČR > Ústav teorie informace a automatizace
Konferenční materiály > Příspěvky z konference
 Záznam vytvořen dne 2018-06-19, naposledy upraven 2018-06-20.


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