11 citations · 39 across the 20 of their papers we have counts for
4 papers · 1 filter
Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes
Yuta Nakahara, Shota Saito, Naoki Ichijo +2
In the field of decision trees, most previous studies have difficulty ensuring the statistical optimality of a prediction of new data and suffer from overfitting because trees are…
Batch Updating of a Posterior Tree Distribution over a Meta-Tree
Yuta Nakahara, Toshiyasu Matsushima
Previously, we proposed a probabilistic data generation model represented by an unobservable tree and a sequential updating method to calculate a posterior distribution over a set…
Probability Distribution on Rooted Trees
Yuta Nakahara, Shota Saito, Akira Kamatsuka +1
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…
Theoretical Analysis of the Advantage of Deepening Neural Networks
Yasushi Esaki, Yuta Nakahara, Toshiyasu Matsushima
We propose two new criteria to understand the advantage of deepening neural networks. It is important to know the expressivity of functions computable by deep neural networks in or…