.png){kind=icon}
返回
Variational oblique predictive clustering trees
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2026.131255.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
• Introduces VSPYCT, a variational Bayesian oblique decision tree for struc- tured output prediction. • Captures parameter uncertainty through variational inference in each tree split. • Matches or outperforms SPYCT ensembles on classification and multi-target regression tasks. • Provides feature importance scores and visual interpretability within a single-tree framework. • Demonstrates robustness to spurious features and performs well across varied dataset properties.
Keyword:
Variational inference
Predictive clustering
Interpretable models
Structured output prediction
Uncertainty quantification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
.png){kind=icon}
Artificial neural network analysis of heat and mass transfer in fractional Casson flow分数Casson流中的传热传质人工神经网络分析OAAI
OAAI分享收藏
Survival analysis as semi-supervised multi-target regression for time-to-employment prediction using oblique predictive clustering trees生存分析作为使用倾斜预测聚类树的就业时间预测的半监督多目标回归OAAI
OAAI分享收藏Mathematical analysis of Prandtl number with Artificial neural network and fractional operator for nanofluid flow基于人工神经网络和分数阶算子对纳米流体流动普朗特数的数学分析OAAI
OAAI分享收藏
查看更多内容