A survey on heterogeneous transfer learning <s> Transfer learning <s> A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. <s> BIB001 </s> A survey on heterogeneous transfer learning <s> Transfer learning <s> Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. <s> BIB002
Transfer learning (TL)  BIB001 BIB002 aims to produce an effective model for a target task with limited or no labeled training data by leveraging and exploiting knowledge from a different, but related source domain to predict the truth label for an unseen target instance. Due to insufficient labeled instances, training a model in this target task would result in degraded performance as compared to a model trained with sufficient labeled data. However, by enhancing the training with supplementary labeled data from a related source domain, the model's ability to classify target instances can be improved. The challenge becomes how to distinguish beneficial knowledge in a source domain from the inherent cross-domain noise, due to the varied distributions, and apply it to a target domain. Transfer learning can be split into two main categories when it comes to the feature spaces: homogeneous and heterogeneous transfer learning.