Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Infinite numbers of real-world applications use Machine Learning (ML) techniques to
develop potentially the best data available for the users. Transfer learning (TL), one of the …

Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

Abstract Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field
based on modern information technology, has gained significant research value and …

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …

For a target task where the labeled data are unavailable, domain adaptation can transfer a
learner from a different source domain. Previous deep domain adaptation methods mainly …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

With the rapid development of computing technology, wearable devices make it easy to get
access to people's health information. Smart healthcare achieves great success by training …

Big data, high-performance computing, and (deep) machine learning are increasingly
becoming key to precision medicine—from identifying disease risks and taking preventive …

Electroencephalography (EEG) measures the neuronal activities in different brain regions
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …