Information-based objective functions for active data selection
Neural Computation
On-line learning in neural networks
On-line learning in neural networks
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
Support Vector Machines for Classification in Nonstandard Situations
Machine Learning
On-line learning in changing environments with applications in supervised and unsupervised learning
Neural Networks - Computational models of neuromodulation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
An introduction to boosting and leveraging
Advanced lectures on machine learning
Importance sampling for reinforcement learning with multiple objectives
Importance sampling for reinforcement learning with multiple objectives
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An Improved Categorization of Classifier's Sensitivity on Sample Selection Bias
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Subspace Information Criterion for Model Selection
Neural Computation
Algebraic Analysis for Nonidentifiable Learning Machines
Neural Computation
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Journal of Machine Learning Research
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Active learning with statistical models
Journal of Artificial Intelligence Research
Semi-Supervised Learning
Uniform convergence of adaptive graph-based regularization
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Statistical active learning in multilayer perceptrons
IEEE Transactions on Neural Networks
Asymptotic Bayesian generalization error when training and test distributions are different
Proceedings of the 24th international conference on Machine learning
Experimental Bayesian Generalization Error of Non-regular Models under Covariate Shift
Neural Information Processing
Theory and algorithm for learning with dissimilarity functions
Neural Computation
Pool-based active learning in approximate linear regression
Machine Learning
Using Rest Class and Control Paradigms for Brain Computer Interfacing
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Efficient Sample Reuse in EM-Based Policy Search
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Adaptive importance sampling with automatic model selection in value function approximation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Knowledge transfer on hybrid graph
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Density Ratio Estimation: A New Versatile Tool for Machine Learning
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Automatic Choice of Control Measurements
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
A Least-squares Approach to Direct Importance Estimation
The Journal of Machine Learning Research
Semi-supervised local fisher discriminant analysis for dimensionality reduction
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Semi-supervised speaker identification under covariate shift
Signal Processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive linear models for regression: Improving prediction when population has changed
Pattern Recognition Letters
How to Explain Individual Classification Decisions
The Journal of Machine Learning Research
Cross validation framework to choose amongst models and datasets for transfer learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Learning non-stationary system dynamics online using Gaussian processes
Proceedings of the 32nd DAGM conference on Pattern recognition
On-line learning: where are we so far?
Ubiquitous knowledge discovery
On-line learning: where are we so far?
Ubiquitous knowledge discovery
Neural Networks
A unifying view on dataset shift in classification
Pattern Recognition
Expert Systems with Applications: An International Journal
Machine-Learning based co-adaptive calibration: a perspective to fight BCI illiteracy
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
A case study on meta-generalising: a Gaussian processes approach
The Journal of Machine Learning Research
Machine-learning-based coadaptive calibration for brain-computer interfaces
Neural Computation
A benchmark dataset to evaluate sensor displacement in activity recognition
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Importance weighted passive learning
Proceedings of the 21st ACM international conference on Information and knowledge management
No bias left behind: covariate shift adaptation for discriminative 3d pose estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
On the hardness of domain adaptation and the utility of unlabeled target samples
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
Personal and Ubiquitous Computing
Semi-supervised learning with density-ratio estimation
Machine Learning
FIDOS: A generalized Fisher based feature extraction method for domain shift
Pattern Recognition
Neural Computation
Pattern Recognition Letters
Counterfactual reasoning and learning systems: the example of computational advertising
The Journal of Machine Learning Research
Robust common spatial filters with a maxmin approach
Neural Computation
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A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase. However, this assumption is not satisfied, for example, when the outside of the training region is extrapolated. The situation where the training input points and test input points follow different distributions while the conditional distribution of output values given input points is unchanged is called the covariate shift. Under the covariate shift, standard model selection techniques such as cross validation do not work as desired since its unbiasedness is no longer maintained. In this paper, we propose a new method called importance weighted cross validation (IWCV), for which we prove its unbiasedness even under the covariate shift. The IWCV procedure is the only one that can be applied for unbiased classification under covariate shift, whereas alternatives to IWCV exist for regression. The usefulness of our proposed method is illustrated by simulations, and furthermore demonstrated in the brain-computer interface, where strong non-stationarity effects can be seen between training and test sessions.