Sugiyama Masashi | Department of Applied Chemistry, Yamanashi University

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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
Approximating the Best Linear Unbiased Estimator of Non-Gaussian Signals with Gaussian Noise
Adaptive importance sampling with automatic model selection in value function approximation (ニューロコンピューティング)
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)
Least Absolute Policy Iteration-A Robust Approach to Value Function Approximation
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)
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)
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)
Modified Newton Approach to Policy Search (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)
Squared-loss Mutual Information Regularization
Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifier
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)
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
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)
Output Divergence Criterion for Active Learning in Collaborative Settings
Output Divergence Criterion for Active Learning in Collaborative Settings
Photochromism of benzylviologens containing methyl groups on pyridinium rings and embedded in solid poly(N-vinyl-2-pyrrolidone) matrix.
Clustering Unclustered Data : Unsupervised Binary Labeling of Two Datasets Having Different Class Balances
Direct Approximation of Quadratic Mutual Information and Its Application to Dependence-Maximization Clustering
Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
Squared-loss Mutual Information Regularization
Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier
Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering
Improving Importance Estimation in Pool-based Batch Active Learning for Approximate Linear Regression

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