Performing accurate building energy prediction (BEP) is one of the most important
foundations for achieving energy resource allocation and developing energy efficiency …

Multiple instance learning (MIL) is a generalization of supervised learning which attempts to
learn a distinctive classifier from bags of instances. This paper addresses the problem of the …

Abstract Sentiment Analysis, also called Opinion Mining, is currently one of the most studied
research fields. Its aim is to analyze publics' sentiments, opinions, attitudes etc., towards …

Online social networks have become extremely popular with the ever-increasing reachability
of internet to the common person. There are millions of tweets, Facebook messages, and …

Abstract Domain adaptation aims to apply knowledge obtained from a labeled source
domain to an unseen target domain from a different distribution. Recently, domain …

Transfer learning is a great technology that can leverage knowledge from label-rich domains
to address problems in similar domains that lack labeled data. Most previous works focus on …

Machine learning techniques that rely on textual features or sentiment lexicons can lead to
erroneous sentiment analysis. These techniques are especially vulnerable to domain …

Transfer learning can transfer knowledge from a source domain to a target domain,
promoting the performance of the model learned from the source data. Sparse coding can …

Transfer learning aims to leverage valuable information in one domain to promote the
learning tasks in the other domain. Some recent studies indicated that the latent information …

Traditional machine learning is generally committed to obtaining classifiers which are well-
performed over unlabeled test data. This usually relies on two critical assumptions: firstly …