arXiv subjects

Maomi Ueno

Publications and source records attributed to Maomi Ueno.

4 recordsLinked to original sources

arXiv2026

Multi-task deep neural network for predicting both nuclear fission yields and their experimental errors in peak-shaped data

The fission product yield (FPY) is crucially important information for numerous nuclear applications. However, the peak-shaped characteristics of FPY data present important challenges for predicting unobservable FPY data. To address these challenges, after applying Multi-task learning models to fission product yield data and their experimental error estimates, we introduce a novel loss function along with incorporation of the odd even effect. Our approach is intended to predict unknown fission yields and the associated experimental error. To demonstrate the effectiveness of our proposed method, we compared our proposed method with conventional methods that learn each dataset independently. Our findings demonstrate that the proposed methods can predict peak shaped data with experimental error estimates more effectively than earlier methods can.

nucl-th↗

arXiv2021

Exact Learning Augmented Naive Bayes Classifier

Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB), which ensures a class variable with no parents. The proposed method is guaranteed to asymptotically estimate the identical class posterior to that of the exactly learned BN. Comparison experiments demonstrated the superior performance of the proposed method.

cs.LG↗

arXiv2012

Learning networks determined by the ratio of prior and data

Recent reports have described that the equivalent sample size (ESS) in a Dirichlet prior plays an important role in learning Bayesian networks. This paper provides an asymptotic analysis of the marginal likelihood score for a Bayesian network. Results show that the ratio of the ESS and sample size determine the penalty of adding arcs in learning Bayesian networks. The number of arcs increases monotonically as the ESS increases; the number of arcs monotonically decreases as the ESS decreases. Furthermore, the marginal likelihood score provides a unified expression of various score metrics by changing prior knowledge.

cs.LG↗

arXiv2012

Robust learning Bayesian networks for prior belief

Recent reports have described that learning Bayesian networks are highly sensitive to the chosen equivalent sample size (ESS) in the Bayesian Dirichlet equivalence uniform (BDeu). This sensitivity often engenders some unstable or undesirable results. This paper describes some asymptotic analyses of BDeu to explain the reasons for the sensitivity and its effects. Furthermore, this paper presents a proposal for a robust learning score for ESS by eliminating the sensitive factors from the approximation of log-BDeu.

cs.LG↗