Covariate Shift Adaptation by Importance Weighted Cross Validation
Explore this paper's citation graph
Summary
This paper proposes a new method called importance weighted cross validation (IWCV), for which its unbiasedness even under the covariate shift is proved, and the IWCV procedure is the only one that can be applied for unbiased classification under covariates.
- Type
- article
- Published
- 2007-12-01
- Cited by
- 1,115
- References
- 77
- OpenAlex
- https://openalex.org/W189742998
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17547265
Keywords
Covariate, Set (abstract data type), Mathematics, Computer science, Statistics
References
- Bioinformatics: The Machine Learning Approach
- Optimum experimental designs
- On Information and Sufficiency
- Learning with Kernels: support vector machines, regularization, optimization, and beyond
- Support Vector Machines and the Bayes Rule in Classification
- Active Learning with Model Selection — Simultaneous Optimization of Sample Points and Models for Trigonometric Polynomial Models
- Support Vector Machines for Classification in Nonstandard Situations
- Active learning algorithm using the maximum weighted log-likelihood estimator
- Semi-Supervised Learning on Riemannian Manifolds
- Monte Carlo: Concepts, Algorithms, and Applications
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- A Stochastic Approximation Method
- Adaptive On-line Classification for EEG-based Brain Computer Interfaces with AAR parameters and band power estimates / Adaptive On-line Classification einer EEG-basierenden Gehirn-Computer Schnittstelle mit Adaptive Autoregressiven und Bandleistungsparametern
- THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
- Learning and evaluating classifiers under sample selection bias
- Importance sampling for reinforcement learning with multiple objectives
- On the Optimality of Prediction‐based Selection Criteria and the Convergence Rates of Estimators
- Nonparametric and Semiparametric Models
- Toward a perception-based theory of probabilistic reasoning with imprecise probabilities
- Asymptotics for and against cross-validation
Cited by
- Robust Expert Systems for more Flexible Real-World Activity Recognition
- Some Bayesian and multivariate analysis methods in statistical machine learning and applications
- Adaptive linear models for regression: Improving prediction when population has changed
- Learning from weakly representative data and applications in spectral image analysis
- On Evaluation Validity in Music Autotagging
- A Review of Performance Variations in SMR-Based Brain−Computer Interfaces (BCIs)
- Knowledge Transfer on Hybrid Graph
- Apprentissage artificiel appliqué à la prévision de trajectoire d'avion. (Machine Learning Applied to Aircraft Trajectory Prediction)
- Discriminative Density-ratio Estimation
- Probabilistic user behavior models in online stores for recommender systems
- Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
- Apprentissage actif par modèles locaux
- Optimal Data Distributions in Machine Learning
- Investigating brief motor imagery for an ERD/ERS based BCI
- A unified approach of control and identification for optimal control of non-linear systems
- Active Learning with Model Selection in Linear Regression
- Agnostic Active Learning Without Constraints
- Accuracy improvement of vision system for mobile robot navigation by finding the energetic center of laser signal
- An Error-Bound-Regularized Sparse Coding for Spatiotemporal Reflectance Fusion
- Cloud screening algorithm for MERIS and CHRIS multispectral sensors
Related papers
- Input-dependent estimation of generalization error under covariate shift
- Deep Residual Learning for Image Recognition
- Discriminative Learning Under Covariate Shift
- A Survey on Transfer Learning
- Towards adaptive classification for BCI
- Dataset Shift in Machine Learning
- Brain-computer interfaces for communication and control.
- A theory of learning from different domains