1 citations · 1 across the 4 of their papers we have counts for
4 papers
A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods
Shota Saito, Yuta Nakahara, Kohei Horinouchi +3
This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtree region-partitioning model with…
Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation
Yuta Nakahara, Shota Saito, Kohei Horinouchi +4
We propose a variable splitting binary tree (VSBT) model based on Bayesian context tree (BCT) models for time series segmentation. Unlike previous applications of BCT models, the t…
An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process
Keito Tajima, Naoki Ichijo, Yuta Nakahara +1
Predictions using a combination of decision trees are known to be effective in machine learning. Typical ideas for constructing a combination of decision trees for prediction are b…
Boosting-Based Sequential Meta-Tree Ensemble Construction for Improved Decision Trees
Ryota Maniwa, Naoki Ichijo, Yuta Nakahara +1
A decision tree is one of the most popular approaches in machine learning fields. However, it suffers from the problem of overfitting caused by overly deepened trees. Then, a meta-…