1 citations · 1 across the 10 of their papers we have counts for
10 papers
Unimodality-Promoting Regularized Learning for Ordinal Regression
Ryoya Yamasaki
Ordinal regression, also called ordinal classification, is classification of ordinal data, in which the underlying target variable is categorical and considered to have a natural o…
Isotonic Bradley-Terry Model for Paired Comparison Data
Ryoya Yamasaki
In this paper, we study prediction problems for paired comparison data, for example, predicting the win probability between two unmatched players and ranking all the players accord…
Approximately Unimodal Likelihood Models for Ordinal Regression
Ryoya Yamasaki
Ordinal regression (OR, also called ordinal classification) is classification of ordinal data, in which the underlying target variable is categorical and considered to have a natur…
Remarks on Loss Function of Threshold Method for Ordinal Regression Problem
Ryoya Yamasaki, Toshiyuki Tanaka
Threshold methods are popular for ordinal regression problems, which are classification problems for data with a natural ordinal relation. They learn a one-dimensional transformati…
Parallel Algorithm for Optimal Threshold Labeling of Ordinal Regression Methods
Ryoya Yamasaki, Toshiyuki Tanaka
Ordinal regression (OR) is classification of ordinal data in which the underlying categorical target variable has a natural ordinal relation for the underlying explanatory variable…
Convergence Analysis of Blurring Mean Shift
Ryoya Yamasaki, Toshiyuki Tanaka
Blurring mean shift (BMS) algorithm, a variant of the mean shift algorithm, is a kernel-based iterative method for data clustering, where data points are clustered according to the…
Label Smoothing is Robustification against Model Misspecification
Ryoya Yamasaki, Toshiyuki Tanaka
Label smoothing (LS) adopts smoothed targets in classification tasks. For example, in binary classification, instead of the one-hot target used in conventional logisti…
Convergence Analysis of Mean Shift
Ryoya Yamasaki, Toshiyuki Tanaka
The mean shift (MS) algorithm seeks a mode of the kernel density estimate (KDE). This study presents a convergence guarantee of the mode estimate sequence generated by the MS algor…
Optimal Kernel for Kernel-Based Modal Statistical Methods
Ryoya Yamasaki, Toshiyuki Tanaka
Kernel-based modal statistical methods include mode estimation, regression, and clustering. Estimation accuracy of these methods depends on the kernel used as well as the bandwidth…
Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm
Ryoya Yamasaki, Toshiyuki Tanaka
Modal linear regression (MLR) is a method for obtaining a conditional mode predictor as a linear model. We study kernel selection for MLR from two perspectives: "which kernel achie…