Sugiyama Masashi | Department Of Chemistry Faculty Of Science Tokyo University Of Science

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概要

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著作論文

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
A New Meta-Criterion for Regularized Subspace Information Criterion
Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
Methods of cross-domain object matching (情報論的学習理論と機械学習)
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
Recent Advances and Trends in Large-Scale Kernel Methods
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)
Optimal design of regularization term and regularization parameter by subspace information criterion
Information-maximization clustering: analytic solution and model selection (情報論的学習理論と機械学習)
New feature selection method for reinforcement learning: conditional mutual information reveals implicit state-reward dependency (情報論的学習理論と機械学習)
Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation
Independent component analysis by direct density-ratio estimation (ニューロコンピューティング)
A New Meta-Criterion for Regularized Subspace Information Criterion(Pattern Recognition)
Spectral Methods for Thesaurus Construction
Adaptive importance sampling with automatic model selection in reward weighted regression (ニューロコンピューティング)
SERAPH: semi-supervised metric learning paradigm with hyper sparsity (情報論的学習理論と機械学習)
Analysis and improvement of policy gradient estimation (情報論的学習理論と機械学習)
Direct density-ratio estimation with dimensionality reduction via hetero-distributional subspace analysis (情報論的学習理論と機械学習)
Output divergence criterion for active learning in collaborative settings (数理モデル化と問題解決・バイオ情報学)
Estimation of squared-loss mutual information from paired and unpaired samples (情報論的学習理論と機械学習)
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)
Canonical dependency analysis based on squared-loss mutual information (情報論的学習理論と機械学習)
Artist agent A[2]: stroke painterly rendering based on reinforcement learning (パターン認識・メディア理解)
Artist agent A[2]: stroke painterly rendering based on reinforcement learning (情報論的学習理論と機械学習)
Generalization Error Estimation for Non-linear Learning Methods(Neural Networks and Bioengineering)
Improving Precision of the Subspace Information Criterion(Neural Networks and Bioengineering)
Canonical dependency analysis based on squared-loss mutual information (パターン認識・メディア理解)
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (情報論的学習理論と機械学習)
Modified Newton Approach to Policy Search (情報論的学習理論と機械学習)
Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier (情報論的学習理論と機械学習)
Relative Density-Ratio Estimation for Robust Distribution Comparison (情報論的学習理論と機械学習)
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
Modified Newton Approach to Policy Search
Squared-loss Mutual Information Regularization
Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier
Feature Selection via l_1-Penalized Squared-Loss Mutual Information
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (情報論的学習理論と機械学習)
Relative Density-Ratio Estimation for Robust Distribution Comparison
Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering (情報論的学習理論と機械学習)
Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier (情報論的学習理論と機械学習)
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration (情報論的学習理論と機械学習)

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