Optimization of Cargo Shipping Adaptability Modeling Evaluation Based on Bayesian Network Algorithm

Gao, Siyuan; Zhang, Fengrong; Ning, Wei; Wu, Dayong

doi:10.3390/su141912856

Cited by 1 publication

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its objective is to maximize or minimize the value of the objective function by selecting the next sample point at each iteration, without explicitly inferring the mathematical expression of the objective function. Due to considering uncertainty at each iteration, it can discover global optima even with a limited number of sample points, thereby avoiding being trapped in local optima [28] . Given its capability to handle uncertainty and efficiently explore the search space, we employ the Bayesian optimization algorithm to optimize the regularization coefficient of matrices in LDA.…”

Section: Bayesian Optimization-based Ldamentioning

confidence: 99%

Revolution of Shakespearean Plays' Genre Research: Exploring New Avenues through Machine Learning and Shapley Value Analysis

2023

langl

View full text Add to dashboard Cite

In the realm of literary research, the challenges of being confined to narrow niches and disconnected from broader contexts have been long-standing. In response, the integration of digital research methods into literary studies has emerged as a compelling area of exploration. Among the classic subjects of literary research, the classification of Shakespearean drama genres holds particular significance. In this paper, we present a case study focused on introducing a promising predictive and analytical method, which leverages Linear Discriminant Analysis (LDA) and the Shapley value. Our methodology begins by employing decision trees to reduce the dimensionality of textual data. Subsequently, a LDA based on Bayesian optimization algorithm is applied to predict the genres of texts. Finally, we utilize the Shapley value to analyze the important words within the texts and unveil their profound literary associations with respective genres. By adopting this approach, our research contributes to the widespread adoption and digital transformation of literary studies, thereby pioneering new avenues in Shakespearean drama research.

show abstract

Section: Bayesian Optimization-based Ldamentioning

confidence: 99%

Revolution of Shakespearean Plays' Genre Research: Exploring New Avenues through Machine Learning and Shapley Value Analysis

2023

langl

View full text Add to dashboard Cite

show abstract

Optimization of Cargo Shipping Adaptability Modeling Evaluation Based on Bayesian Network Algorithm

Cited by 1 publication

References 47 publications

Revolution of Shakespearean Plays' Genre Research: Exploring New Avenues through Machine Learning and Shapley Value Analysis

Revolution of Shakespearean Plays' Genre Research: Exploring New Avenues through Machine Learning and Shapley Value Analysis

Contact Info

Product

Resources

About