Yamada Makoto | Tokyo Inst. Of Technol.

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概要

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• 同名の論文著者
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関連著者

• Yamada Makoto

  Tokyo Inst. Of Technol.

• YAMADA Makoto

  Tokyo Institute of Technology

• Yamada Makoto

  Department Of Chemistry And Biomolecular Science Toho University

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

• Sugiyama Masashi

  Department of Applied Chemistry, Yamanashi University

• Wichern Gordon

  Mit Lincoln Laboratory

• Sugiyama Masashi

  Tokyo Inst. Of Technol.

• Yamada Makoto

  Department Of Chemistry Faculty Of Science Okayama University Of Science

• Sugiyama Masashi

  Department Of Computer Science Tokyo Institute Of Technology

• Simm Jaak

  Department Of Computer Science Tokyo Institute Of Technology

• Wichern Gordon

  Mit Lincoln Lab.

• Sugiyama Masashi

  Tokyo Inst. Of Technol. Tokyo Jpn

• Hachiya Hirotaka

  Department Of Computer Science Tokyo Institute Of Technology

• SUGIYAMA Masashi

  Department of Computer Science, Tokyo Institute of Technology

• YAMADA MAKOTO

  Department of Surgery, School of Medicine, Showa University

• SIMM Jaak

  Department of Computer Science, Tokyo Institute of Technology

• SIMM JAAK

  Tokyo Institute of Technology

• Hachiya Hirotaka

  Tokyo Inst. Of Technol.

• Kimura Manabu

  Department Of Materials Science And Engineering Metal Section Nagoya Institute Of Technology

• Sugiyama Masashi

  Tokyo Institute Of Technology

• Suzuki Taiji

  Department Of Mathematical Informatics Graduate School Of Information Science And Technology Univers

• Suzuki Taiji

  University of Tokyo

• Kimura Manabu

  Department Of Computer Science Tokyo Institute Of Technology

• Kanamori Takafumi

  Department Of Computer Science And Mathematical Informatics Nagoya University

• Wichern Gordon

  Arizona State University

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著作論文

• Improving the Accuracy of Least-Squares Probabilistic Classifiers
• Improving the Accuracy of Least-Squares Probabilistic Classifiers
• Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
• Information-maximization clustering: analytic solution and model selection (情報論的学習理論と機械学習)
• Direct Importance Estimation with Gaussian Mixture Models
• Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise (情報論的学習理論と機械学習)
• Improving the Accuracy of Least-Squares Probabilistic Classifiers
• Relative Density-Ratio Estimation for Robust Distribution Comparison (情報論的学習理論と機械学習)
• On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion

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