Long short-term memory (LSTM) is one of the most widely used recurrent neural network.
Traditionally, it adopts an offline batch mode for model training. To be updated with new …

Domain adaptation tries to adapt a model trained from a source domain to a different but
related target domain. Currently, prevailing methods for domain adaptation rely on either …

Abstract Domain adaptation (DA) has become a widely used technique for cross-scene
hyperspectral image (HSI) classification. Most DA methods aim to learn a domain invariant …

Bias in datasets can be very detrimental for appropriate statistical estimation. In response to
this problem, importance weighting methods have been developed to match any biased …

As a promising step, the performance of data analysis and feature learning are able to be
improved if certain pattern matching mechanism is available. One of the feasible solutions …

In many real-world applications, the performance of machine learning models is often
significantly degraded because of the covariate shift problem. Appropriately dealing with this …

In traditional machine learning, it is assumed that training data conforming to the stationary
distribution of test data is readily available. Yet, such an assumption is not valid in practice …

In the present paper we introduce new optimization algorithms for the task of density ratio
estimation. More precisely, we consider extending the well-known KMM method using the …

Cross-domain classification refers to completing the corresponding classification task in a
target domain which lacks label information, by exploring useful knowledge in a related …

Machine Learning should be able to support decision-making by focusing on purely logical
conclusions based on historical data. If this data is biased, however, that bias will be …