A survey on heterogeneous transfer learning <s>  <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>  <s> In Traditional Chinese Medicine(TCM) area, medical records are the objective record of a doctor's diagnosis and treatment and they are the basis of the TCM development. However, existing medical records of TCM are derived from books, medical cases, Web and most of them lack the categories information. In this paper, we propose a text classification method for the TCM medical record based on cross-domain topic model. First, we transform the physical books into the digital documents, then tokenize and filter the documents with domain lexicons to achieve the significative sequences of words which largely maintain the topics of original documents. Second, we use the cross domain topic model named Topic Relevance Weighting Model(TRWM) to generate the features. Finally, the generated features are leveraged for the medical records classification and compared with the baselines. The experimental results validate the effectiveness of our method. <s> BIB002
Traditional machine learning operates under the assumption that the training and testing data are taken from the same input feature space and the same data distribution  . However, this assumption may not hold when faced with real-world scenarios. The feature space may differ in the manner that the training data may hold a specific set of features, but the testing data may have a feature space of different dimensions, or its features may represent different attributes entirely. The data distribution may also differ in the manner that, if the training and testing data were collected from different domains, the marginal and/or conditional probability distributions  may differ. As an example, consider the case described in BIB002 , where a model is trained to classify records of Book A into predefined categories and is then tested using records from Book B. In this case, the model will have degraded performance because the training and testing data were taken from different data distributions, since each book has a different variety of words, sentences, etc. When the distribution changes, most statistical models need to be rebuilt completely using new labeled training data from the new distribution. This process is often expensive and difficult due to the effort of collecting sufficient labeled data to train an effective new model BIB001 . Thus, there is a need for a method to create a high-performance model for a target domain in a different distribution without such a significant amount of labeling effort. This can be achieved using transfer learning.