Getting labeled data in each domain is always an expensive and a time consuming task. Hence, cross-domain sentiment analysis has emerged as a demanding research area where a labeled source domain facilitates classifier for an unlabeled target domain. However, cross-domain sentiment analysis is still a challenging task because of the differences across domains. A word which is used with positive...

Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer learning framework where the source and target domains are related through t...

The key question in transfer learning (TL) research is how to make model induction transferable across different domains. Common methods so far require source and target domains to have a shared/homogeneous feature space, or the projection of features from heterogeneous domains onto a shared space. This paper proposes a novel framework, which does not require a shared feature space but instead ...

This paper considers the problem of transferring experimental findings learned from multiple heterogeneous domains to a different environment, in which only passive observations can be collected. Pearl and Bareinboim (2011) established a complete characterization for such transfer between two domains, a source and a target, and this paper generalizes their results to multiple heterogeneous doma...

OBJECTIVE To develop and validate domains to assess attendee's gain at sexually transmitted infection (STI) clinic and their understanding after utilizing services at STI Clinic. METHODS Study was done in two phases. In-depth interviews were conducted to explore attendee's perception. Domains generated through the first phase were validated by conducting another 50 structured interviews. RE...

Analogical reasoning is an acknowledged process behind many episodes of creativity. Typically, the creator chances upon information unrelated to the given problem – and solves the problem by analogy with this accidental source of inspiration. Current models of analogical retrieval do not explain how semantically unrelated source domains are retrieved. We present the RADAR algorithm that maps do...

, © Li et al. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and redistribution in any medium, provided that the original author and source are credited. The manuscript “Regions of very low H3K27me3 partition the Drosophila genome into topological domains” aims to investigate the mechanisms...

Data-driven dictionaries have produced state-of-theart results in various classification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary. Specifically, we describe a technique which join...