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pro vyhledávání: '"Yamasaki, Ryoya"'
Autor:
Yamasaki, Ryoya
甲第25440号
情博第878号
新制||情||147(附属図書館)
学位規則第4条第1項該当
Doctor of Informatics
Kyoto University
DFAM
情博第878号
新制||情||147(附属図書館)
学位規則第4条第1項該当
Doctor of Informatics
Kyoto University
DFAM
Externí odkaz:
http://hdl.handle.net/2433/288874
Autor:
Yamasaki, Ryoya, Tanaka, Toshiyuki
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. For $K$-class OR tasks, threshold methods learn a one-dimensional tr
Externí odkaz:
http://arxiv.org/abs/2405.12756
Autor:
Yamasaki, Ryoya, Tanaka, Toshiyuki
Threshold methods are popular for ordinal regression problems, which are classification problems for data with a natural ordinal relation. They learn a one-dimensional transformation (1DT) of observations of the explanatory variable, and then assign
Externí odkaz:
http://arxiv.org/abs/2405.13288
Autor:
Yamasaki, Ryoya, Tanaka, Toshiyuki
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 their convergent points via iterative blurring. In this paper, we analyze
Externí odkaz:
http://arxiv.org/abs/2402.15146
Autor:
Yamasaki, Ryoya, Tanaka, Toshiyuki
Label smoothing (LS) adopts smoothed targets in classification tasks. For example, in binary classification, instead of the one-hot target $(1,0)^\top$ used in conventional logistic regression (LR), LR with LS (LSLR) uses the smoothed target $(1-\fra
Externí odkaz:
http://arxiv.org/abs/2305.08501
Autor:
Yamasaki, Ryoya, Tanaka, Toshiyuki
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 algorithm and an evaluation of the convergence rate, under fairly mild cond
Externí odkaz:
http://arxiv.org/abs/2305.08463
Autor:
Yamasaki, Ryoya, Tanaka, Toshiyuki
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. We study effect of the selection of the kernel function to the estim
Externí odkaz:
http://arxiv.org/abs/2304.10046
Autor:
Yamasaki, Ryoya, Tanaka, Toshiyuki
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 achieves smaller error?" and "which kernel is computationally efficient?".
Externí odkaz:
http://arxiv.org/abs/2001.11168
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