Year: 2025
Type: article
Abstract: The hierarchical and recursive expressive capability of rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning. On the other hand, such hierarchical expressive capability causes a problem to avoid overfitting. One unified approach to solve this is a Bayesian approach, in which the rooted tree is regarded as a random variable and a direct loss function can be assumed on the selected model or the predicted value for a new data point. However, all the previous studies on this approach are based on the probability distribution on full trees, to the best of our knowledge. In this paper, we propose a generalized probability distribution for any rooted trees in which only the maximum number of child nodes and the maximum depth are fixed. Furthermore, we derive recursive methods to evaluate the characteristics of the probability distribution without any approximations.
Authors: Yuta NAKAHARA, Shota SAITO, Akira KAMATSUKA, Toshiyasu MATSUSHIMA
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: Applying High-Performance Data Compression Method to Data Science: Application to Data with Dependence, パーソナルデータの組織横断利活用のための安全性理論および実証研究, Construction of Bayesian Optimal Decision Tree Algorithm Using Metatree and Latent Class Tree by Information Theory, Foundations of Security Quantification by Integrating Information, Computation, and Cryptography Theories, 文系学生のためのAIプログラミング教育の最適化とオンライン教育システムの構築, 統計的決定理論に基づくプライバシ保護データ解析基盤に関する基礎的研究, ビジネス施策を正しく評価するための潜在構造に基づく協調フィルタリングに関する研究
OpenAlex ID: W7082984596