6 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…
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…