Počet záznamů: 1
Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions
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SYSNO ASEP 0490309 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions Tvůrce(i) Plajner, Martin (UTIA-B)
Vomlel, Jiří (UTIA-B) RID, ORCIDCelkový počet autorů 2 Zdroj.dok. Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18). - Praha : MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University, 2018 / Kratochvíl Václav ; Vejnarová Jiřina - ISBN 978-80-7378-361-7 Rozsah stran s. 153-164 Poč.str. 12 s. Forma vydání Tištěná - P Akce Workshop on Uncertainty Processing (WUPES’18) Datum konání 06.06.2018 - 09.06.2018 Místo konání Třeboň Země CZ - Česká republika Typ akce WRD Jazyk dok. eng - angličtina Země vyd. CZ - Česká republika Klíč. slova Bayesian networks ; Learning model parameters ; monotonicity condition Vědní obor RIV JD - Využití počítačů, robotika a její aplikace Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA16-12010S GA ČR - Grantová agentura ČR Institucionální podpora UTIA-B - RVO:67985556 Anotace 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.
In 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.
The 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.Pracoviště Ústav teorie informace a automatizace Kontakt Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Rok sběru 2019
Počet záznamů: 1