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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…