A survey of transfer learning <s>  <s> The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work. <s> BIB001 </s> A survey of 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> BIB002 </s> A survey of transfer learning <s>  <s> Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. <s> BIB003 </s> A survey of transfer learning <s>  <s> We propose a novel problem formulation of learning a single task when the data are provided in different feature spaces. Each such space is called an outlook, and is assumed to contain both labeled and unlabeled data. The objective is to take advantage of the data from all the outlooks to better classify each of the outlooks. We devise an algorithm that computes optimal affine mappings from different outlooks to a target outlook by matching moments of the empirical distributions. We further derive a probabilistic interpretation of the resulting algorithm and a sample complexity bound indicating how many samples are needed to adequately find the mapping. We report the results of extensive experiments on activity recognition tasks that show the value of the proposed approach in boosting performance. <s> BIB004 </s> A survey of transfer learning <s>  <s> We present a new approach to cross-language text classification that builds on structural correspondence learning, a recently proposed theory for domain adaptation. The approach uses unlabeled documents, along with a simple word translation oracle, in order to induce task-specific, cross-lingual word correspondences. We report on analyses that reveal quantitative insights about the use of unlabeled data and the complexity of inter-language correspondence modeling. ::: ::: We conduct experiments in the field of cross-language sentiment classification, employing English as source language, and German, French, and Japanese as target languages. The results are convincing; they demonstrate both the robustness and the competitiveness of the presented ideas. <s> BIB005 </s> A survey of transfer learning <s>  <s> We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications. <s> BIB006 </s> A survey of transfer learning <s>  <s> In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features can change from one dataset to the next. In this paper, we address the problem of visual domain adaptation for transferring object models from one dataset or visual domain to another. We introduce ARC-t, a flexible model for supervised learning of non-linear transformations between domains. Our method is based on a novel theoretical result demonstrating that such transformations can be learned in kernel space. Unlike existing work, our model is not restricted to symmetric transformations, nor to features of the same type and dimensionality, making it applicable to a significantly wider set of adaptation scenarios than previous methods. Furthermore, the method can be applied to categories that were not available during training. We demonstrate the ability of our method to adapt object recognition models under a variety of situations, such as differing imaging conditions, feature types and codebooks. <s> BIB007 </s> A survey of transfer learning <s>  <s> Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization <s> BIB008 </s> A survey of transfer learning <s>  <s> We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods. <s> BIB009 </s> A survey of transfer learning <s>  <s> In this paper, we present an efficient multi-class heterogeneous domain adaptation method, where data from source and target domains are represented by heterogeneous features of different dimensions. Specifically, we propose to reconstruct a sparse feature transformation matrix to map the weight vector of classifiers learned from the source domain to the target domain. We cast this learning task as a compressed sensing problem, where each binary classifier induced from multiple classes can be deemed as a measurement sensor. Based on the compressive sensing theory, the estimation error of the transformation matrix decreases with the increasing number of classifiers. Therefore, to guarantee reconstruction performance, we construct sufficiently many binary classifiers based on the error correcting output coding. Extensive experiments are conducted on both a toy dataset and three real-world datasets to verify the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy. <s> BIB010 </s> A survey of transfer learning <s>  <s> Most previous heterogeneous transfer learning methods learn a cross-domain feature mapping between heterogeneous feature spaces based on a few cross-domain instance-correspondences, and these corresponding instances are assumed to be representative in the source and target domains respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precise due to the bias issue of the correspondences in the target or (and) source domain(s). In this case, a classifier trained on the labeled transformed-source-domain data may not be useful for the target domain. In this paper, we present a new transfer learning framework called Hybrid Heterogeneous Transfer Learning (HHTL), which allows the corresponding instances across domains to be biased in either the source or target domain. Specifically, we propose a deep learning approach to learn a feature mapping between cross-domain heterogeneous features as well as a better feature representation for mapped data to reduce the bias issue caused by the cross-domain correspondences. Extensive experiments on several multilingual sentiment classification tasks verify the effectiveness of our proposed approach compared with some baseline methods. <s> BIB011 </s> A survey of transfer learning <s>  <s> Software defect prediction is one of the most active research areas in software engineering. We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP). Researchers also proposed cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. In recent studies, CPDP is proved to be feasible. However, CPDP requires projects that have the same metric set, meaning the metric sets should be identical between projects. As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets. To address the limitation, we propose heterogeneous defect prediction (HDP) to predict defects across projects with heterogeneous metric sets. Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets. Our empirical study on 28 subjects shows that about 68% of predictions using our approach outperform or are comparable to WPDP with statistical significance. <s> BIB012
to play the piano BIB002 . One person is able to take information from a previously learned task and use it in a beneficial way to learn a related task. Looking at a concrete example from the domain of machine learning, consider the task of predicting text sentiment of product reviews where there exists an abundance of labeled data from digital camera reviews. If the training data and the target data are both derived from digital camera reviews, then traditional machine learning techniques are used to achieve good prediction results. However, in the case where the training data is from digital camera reviews and the target data is from food reviews, then the prediction results are likely to degrade due to the differences in domain data. Digital camera reviews and food reviews still have a number of characteristics in common, if not exactly the same. They both are written in textual form using the same language, and they both express views about a purchased product. Because these two domains are related, transfer learning can be used to potentially improve the results of a target learner BIB002 . An alternative way to view the data domains in a transfer learning environment is that the training data and the target data exist in different sub-domains linked by a high-level common domain. For example, a piano player and a guitar player are subdomains of a musician domain. Further, a digital camera review and a food review are subdomains of a review domain. The high-level common domain determines how the subdomains are related. As previously mentioned, the need for transfer learning occurs when there is a limited supply of target training data. This could be due to the data being rare, the data being expensive to collect and label, or the data being inaccessible. With big data repositories becoming more prevalent, using existing datasets that are related to, but not exactly the same as, a target domain of interest makes transfer learning solutions an attractive approach. There are many machine learning applications that transfer learning has been successfully applied to including text sentiment classification BIB006 , image classification BIB009 BIB007 BIB003 , human activity classification BIB004 , software defect classification BIB012 , and multi-language text classification BIB010 BIB005 BIB011 . This survey paper aims to provide a researcher interested in transfer learning with an overview of related works, examples of applications that are addressed by transfer learning, and issues and solutions that are relevant to the field of transfer learning. This survey paper provides an overview of current methods being used in the field of transfer learning as it pertains to data mining tasks for classification, regression, and clustering problems; however, it does not focus on transfer learning for reinforcement learning (for more information on reinforcement learning see Taylor BIB001 ). Information pertaining to the history and taxonomy of transfer learning is not provided in this survey paper, but can be found in the paper by Pan BIB002 . Since the publication of the transfer learning survey paper by Pan BIB002 in 2010, there have been over 700 academic papers written addressing advancements and innovations on the subject of transfer learning. These works broadly cover the areas of new algorithm development, improvements to existing transfer learning algorithms, and algorithm deployment in new application domains. The selected surveyed works in this paper are meant to be diverse and representative of transfer learning solutions in the past 5 years. Most of the surveyed papers provide a generic transfer learning solution; however, some surveyed papers provide solutions that are specific to individual applications. This paper is written with the assumption the reader has a working knowledge of machine learning. For more information on machine learning see Witten BIB008 . The surveyed works in this paper are intended to present a high-level description of proposed solutions with unique and salient points being highlighted. Experiments from the surveyed papers are described with respect to applied applications, other competing solutions tested, and overall relative results of the experiments. This survey paper provides a section on heterogeneous transfer learning which, to the best of our knowledge, is unique. Additionally, a list of software downloads for various surveyed papers is provided, which is unique to this paper. The remainder of this paper is organized as follows. "Definitions of transfer learning" section provides definitions and notations of transfer learning. "Homogeneous transfer learning" and "Heterogeneous transfer learning" sections provide solutions on homogeneous and heterogeneous transfer learning, "Negative transfer" section provides information on negative transfer as it pertains to transfer learning. "Transfer learning application" section provides examples of transfer learning applications. "Conclusion and discussion" section summarizes and discusses potential future research work. Appendix provides information on software downloads for transfer learning.