SUGIYAMA Masashi | Department of Computer Science, Tokyo Institute of Technology

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概要

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著作論文

Recent Advances and Trends in Large-Scale Kernel Methods
Statistical active learning for efficient value function approximation in reinforcement learning (ニューロコンピューティング)
Improving the Accuracy of Least-Squares Probabilistic Classifiers
Improving the Accuracy of Least-Squares Probabilistic Classifiers
Least Absolute Policy Iteration — A Robust Approach to Value Function Approximation
A New Meta-Criterion for Regularized Subspace Information Criterion
Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
Multi-task learning with least-squares probabilistic classifiers (パターン認識・メディア理解)
Multi-task learning with least-squares probabilistic classifiers (情報論的学習理論と機械学習)
Adaptive importance sampling with automatic model selection in value function approximation (ニューロコンピューティング)
Analytic Optimization of Adaptive Ridge Parameters Based on Regularized Subspace Information Criterion(Neural Networks and Bioengineering)
Adaptive Ridge Learning in Kernel Eigenspace and Its Model Selection
Syntheses of New Artificial Zinc Finger Proteins Containing Trisbipyridine-ruthenium Amino Acid at The N-or C-terminus as Fluorescent Probes
Analytic Optimization of Shrinkage Parameters Based on Regularized Subspace Information Criterion(Neural Networks and Bioengineering)
Constructing Kernel Functions for Binary Regression(Pattern Recognition)
Clustering Unclustered Data : Unsupervised Binary Labeling of Two Datasets Having Different Class Balances

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