A density ratio approach to two-sample test (パターン認識・メディア理解)
スポンサーリンク
概要
- 論文の詳細を見る
The goal of the two-sample test (a.k.a. the homogeneity test) is, given two sets of samples, to judge whether the probability distributions behind the samples are the same or not. In this paper, we propose a novel non-parametric method of two-sample test based on a least-squares density ratio estimator. Through various experiments, we show that the proposed method overall produces smaller type-II error (i.e., the rate of judging the two distributions to be the same when they are actually different) than a state-of-the-art method, with slightly larger type-I error (i.e., the rate of judging the two distributions to be different when they are actually the same).
- 社団法人電子情報通信学会の論文
- 2010-08-29
著者
-
Sugiyama Masashi
Tokyo Inst. Of Technol.
-
Kimura Manabu
Tokyo Institute Of Technology
-
KANAMORI Takafumi
Nagoya University
-
Suzuki Taiji
University of Tokyo
-
Itoh Yuta
Tokyo Institute of Technology
-
Kimura Manabu
Tokyo Inst. Of Technol.
関連論文
- Statistical active learning for efficient value function approximation in reinforcement learning (ニューロコンピューティング)
- Lighting Condition Adaptation for Perceived Age Estimation
- Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers
- A Unified Framework of Density Ratio Estimation under Bregman Divergence
- Adaptive importance sampling with automatic model selection in value function approximation (ニューロコンピューティング)
- Improving Model-based Reinforcement Learning with Multitask Learning
- Improving Model-based Reinforcement Learning with Multitask Learning
- Least-Squares Conditional Density Estimation
- Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
- カーネル密度比推定の統計的解析(学習問題の解析,テキスト・Webマイニング,一般)
- A Semi-Supervised Approach to Perceived Age Prediction from Face Images
- Conditional Density Estimation Based on Density Ratio Estimation
- Conditional Density Estimation Based on Density Ratio Estimation
- A density ratio approach to two-sample test (パターン認識・メディア理解)
- A density ratio approach to two-sample test (情報論的学習理論と機械学習)
- Theoretical Analysis of Density Ratio Estimation
- Independent component analysis by direct density-ratio estimation (ニューロコンピューティング)
- Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability(Artificial Intelligence and Cognitive Science)
- FOREWORD
- Superfast-Trainable Multi-Class Probabilistic Classifier by Least-Squares Posterior Fitting
- Direct Importance Estimation with Gaussian Mixture Models
- Improving the Accuracy of Least-Squares Probabilistic Classifiers
- 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)
- Least-Squares Independence Test
- Density Difference Estimation (情報論的学習理論と機械学習)
- Density-ratio matching under the Bregman divergence : a unified framework of density-ratio estimation
- Multiscale Bagging and Its Applications
- Relative Density-Ratio Estimation for Robust Distribution Comparison (情報論的学習理論と機械学習)
- Density Difference Estimation
- Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature Engineering
- Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
- Early stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic Classifier
- Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic Classifiers
- Multi-Task Approach to Reinforcement Learning for Factored-State Markov Decision Problems
- Constrained Least-Squares Density-Difference Estimation
- A Density-ratio Framework for Statistical Data Processing
- Computationally Efficient Multi-task Learning with Least-squares Probabilistic Classifiers
- Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation
- A Density-ratio Framework for Statistical Data Processing
- FOREWORD
- On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion