A survey of transfer learning <s> Parameter-based transfer learning <s> While the model parameters of many kernel learning methods are given by the solution of a convex optimisation problem, the selection of good values for the kernel and regularisation parameters, i.e. model selection, is much less straight-forward. This paper describes a simple and efficient approach to model selection for weighted least-squares support vector machines, and compares a variety of model selection criteria based on leave-one-out cross-validation. An external cross-validation procedure is used for performance estimation, with model selection performed independently in each fold to avoid selection bias. The best entry based on these methods was ranked in joint first place in the WCCI-2006 performance prediction challenge, demonstrating the effectiveness of this approach. <s> BIB001 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model. <s> BIB002 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> Learning a category from few examples is a challenging task for vision algorithms, while psychological studies have shown that humans are able to generalise correctly even from a single instance (one-shot learning). The most accredited hypothesis is that humans are able to exploit prior knowledge when learning a new related category. This paper presents an SVM-based model adaptation algorithm able to perform knowledge transfer for a new category when very limited examples are available. Using a leave- one-out estimate of the weighted error-rate the algorithm automatically decides from where to transfer (on which known category to rely), how much to transfer (the degree of adaptation) and if it is worth transferring something at all. Moreover a weighted least-squares loss function takes optimally care of data unbalance between negative and positive examples. Experiments presented on two different object category databases show that the proposed method is able to exploit previous knowledge avoiding negative transfer. The overall classification performance is increased compared to what would be achieved by starting from scratch. Furthermore as the number of already learned categories grows, the algorithm is able to learn a new category from one sample with increasing precision, i.e. it is able to perform one-shot learning. <s> BIB003 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> Learning object categories from small samples is a challenging problem, where machine learning tools can in general provide very few guarantees. Exploiting prior knowledge may be useful to reproduce the human capability of recognizing objects even from only one single view. This paper presents an SVM-based model adaptation algorithm able to select and weight appropriately prior knowledge coming from different categories. The method relies on the solution of a convex optimization problem which ensures to have the minimal leave-one-out error on the training set. Experiments on a subset of the Caltech-256 database show that the proposed method produces better results than both choosing one single prior model, and transferring from all previous experience in a flat uninformative way. <s> BIB004 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy. <s> BIB005 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. The effectiveness of the transfer is affected by the relationship between source and target. Rather than improving the learning, brute force leveraging of a source poorly related to the target may decrease the classifier performance. One strategy to reduce this negative transfer is to import knowledge from multiple sources to increase the chance of finding one source closely related to the target. This work extends the boosting framework for transferring knowledge from multiple sources. Two new algorithms, MultiSource-TrAdaBoost, and TaskTrAdaBoost, are introduced, analyzed, and applied for object category recognition and specific object detection. The experiments demonstrate their improved performance by greatly reducing the negative transfer as the number of sources increases. TaskTrAdaBoost is a fast algorithm enabling rapid retraining over new targets. <s> BIB006 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade. <s> BIB007 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> Feature trajectories have shown to be efficient for representing videos. Typically, they are extracted using the KLT tracker or matching SIFT descriptors between frames. However, the quality as well as quantity of these trajectories is often not sufficient. Inspired by the recent success of dense sampling in image classification, we propose an approach to describe videos by dense trajectories. We sample dense points from each frame and track them based on displacement information from a dense optical flow field. Given a state-of-the-art optical flow algorithm, our trajectories are robust to fast irregular motions as well as shot boundaries. Additionally, dense trajectories cover the motion information in videos well. We, also, investigate how to design descriptors to encode the trajectory information. We introduce a novel descriptor based on motion boundary histograms, which is robust to camera motion. This descriptor consistently outperforms other state-of-the-art descriptors, in particular in uncontrolled realistic videos. We evaluate our video description in the context of action classification with a bag-of-features approach. Experimental results show a significant improvement over the state of the art on four datasets of varying difficulty, i.e. KTH, YouTube, Hollywood2 and UCF sports. <s> BIB008 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> We consider the characterization of muscle fatigue through noninvasive sensing mechanism such as surface electromyography (SEMG). While changes in the properties of SEMG signals with respect to muscle fatigue have been reported in the literature, the large variation in these signals across different individuals makes the task of modeling and classification of SEMG signals challenging. Indeed, the variation in SEMG parameters from subject to subject creates differences in the data distribution. In this paper, we propose a transfer learning framework based on the multi-source domain adaptation methodology for detecting different stages of fatigue using SEMG signals, that addresses the distribution differences. In the proposed framework, the SEMG data of a subject represent a domain; data from multiple subjects in the training set form the multiple source domains and the test subject data form the target domain. SEMG signals are predominantly different in conditional probability distribution across subjects. The key feature of the proposed framework is a novel weighting scheme that addresses the conditional probability distribution differences across multiple domains (subjects). We have validated the proposed framework on Surface Electromyogram signals collected from 8 people during a fatigue-causing repetitive gripping activity. Comprehensive experiments on the SEMG data set demonstrate that the proposed method improves the classification accuracy by 20% to 30% over the cases without any domain adaptation method and by 13% to 30% over the existing state-of-the-art domain adaptation methods. <s> BIB009 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> Recent work has demonstrated the effectiveness of domain adaptation methods for computer vision applications. In this work, we propose a new multiple source domain adaptation method called Domain Selection Machine (DSM) for event recognition in consumer videos by leveraging a large number of loosely labeled web images from different sources (e.g., Flickr.com and Photosig.com), in which there are no labeled consumer videos. Specifically, we first train a set of SVM classifiers (referred to as source classifiers) by using the SIFT features of web images from different source domains. We propose a new parametric target decision function to effectively integrate the static SIFT features from web images/video keyframes and the spacetime (ST) features from consumer videos. In order to select the most relevant source domains, we further introduce a new data-dependent regularizer into the objective of Support Vector Regression (SVR) using the ∊-insensitive loss, which enforces the target classifier shares similar decision values on the unlabeled consumer videos with the selected source classifiers. Moreover, we develop an alternating optimization algorithm to iteratively solve the target decision function and a domain selection vector which indicates the most relevant source domains. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed method DSM over the state-of-the-art by a performance gain up to 46.41%. <s> BIB010 </s> A survey of transfer learning <s> Parameter-based transfer learning <s> In this paper, we propose a new framework called domain adaptation machine (DAM) for the multiple source domain adaption problem. Under this framework, we learn a robust decision function (referred to as target classifier) for label prediction of instances from the target domain by leveraging a set of base classifiers which are prelearned by using labeled instances either from the source domains or from the source domains and the target domain. With the base classifiers, we propose a new domain-dependent regularizer based on smoothness assumption, which enforces that the target classifier shares similar decision values with the relevant base classifiers on the unlabeled instances from the target domain. This newly proposed regularizer can be readily incorporated into many kernel methods (e.g., support vector machines (SVM), support vector regression, and least-squares SVM (LS-SVM)). For domain adaptation, we also develop two new domain adaptation methods referred to as FastDAM and UniverDAM. In FastDAM, we introduce our proposed domain-dependent regularizer into LS-SVM as well as employ a sparsity regularizer to learn a sparse target classifier with the support vectors only from the target domain, which thus makes the label prediction on any test instance very fast. In UniverDAM, we additionally make use of the instances from the source domains as Universum to further enhance the generalization ability of the target classifier. We evaluate our two methods on the challenging TRECIVD 2005 dataset for the large-scale video concept detection task as well as on the 20 newsgroups and email spam datasets for document retrieval. Comprehensive experiments demonstrate that FastDAM and UniverDAM outperform the existing multiple source domain adaptation methods for the two applications. <s> BIB011
The paper by Tommasi BIB004 addresses the transfer learning environment characterized by limited labeled target data and multiple labeled source domains where each source corresponds to a particular class. In this case, each source is able to build a binary learner to predict that class. The objective is to build a target binary learner for a new class using minimal labeled target data and knowledge transferred from the multiple source learners. An algorithm is proposed to transfer the SVM hyperplane information of each of the source learners to the new target learner. To minimize the effects of negative transfer, the information transferred from each source to the target will be weighted such that the most related source domains receive the highest weighting. The weights are determined through a leave out one process as defined by Cawley BIB001 . The Tommasi BIB004 approach, called the multi-model knowledge transfer (MMKT) method, extends the method proposed by Tommasi BIB003 that only transfers a single source domain. Experiments are performed on the application of image recognition where classification accuracy is measured as the performance metric. Transfer learning methods tested include an average weight approach (same as Tommasi BIB004 but all source weights are equal), and the Tommasi BIB003 approach. A baseline approach is tested, which is trained on the limited labeled target data. The best performing method is Tommasi BIB004 , followed by the average weight, Tommasi BIB003 , and the baseline approach. As the number of labeled target instances goes up, the Tommasi BIB004 and average weight methods converge to the same performance. This is because the adverse effects of negative transfer are lessened as the labeled target data increases. This result demonstrates the Tommasi BIB004 approach is able to lessen the effects of negative transfer from unrelated sources. The transfer learning approach presented in the paper by Duan BIB010 , referred to as the Domain Selection Machine (DSM), is tightly coupled to the application of event recognition in consumer videos. Event recognition in videos is the process of predicting the occurrence of a particular event or topic (e.g. "show" or "performance") in a given video. In this scenario, the target domain is unlabeled and the source information is obtained from annotated images found via web searches. For example, a text query of the event "show" for images on Photosig.com represents one source and the same query on Flickr. com represents another separate source. The domain selection machine proposed in this paper is realized as follows. For each individual source, an SVM classifier is created using SIFT (Lowe BIB007 ) image features. The final target classifier is made up of two parts. The first part is a weighted sum of the source classifier outputs whose input is the SIFT features from key frames of the input video. The second part is a learning function whose inputs are space-time features BIB008 from the input video and is trained from target data where the target labels are estimated (pseudo labels) from the weighted sum of the source classifiers. To combat the effects of negative transfer from unrelated sources, the most relevant source domains are selected by using an alternating optimization algorithm that iteratively solves the target decision function and the domain selection vector. Experiments are performed in the application of event recognition in videos as described above where the mean average precision is measured as the performance metric. A baseline method is created by training a separate SVM classifier on each source domain and then equally combining the classifiers. The other transfer learning approaches tested include the approach by Bruzzone BIB005 , Schweikert  , Duan BIB011 , and Chattopadhyay BIB009 . The Duan BIB011 approach outperforms all the other approaches tested. The other approaches all have similar results, meaning the transfer learning methods did not outperform the baseline approach. The possible reason for this result is the existence of unrelated sources in the experiment. The other transfer learning approaches tested had no mechanism to guard against negative transfer from unrelated sources. The paper by Yao BIB006 first presents an instance-based transfer learning approach followed by a separate parameter-based transfer learning approach. In the transfer learning process, if the source and target domains are not related enough, negative transfer can occur. Since it is difficult to measure the relatedness between any particular source and target domain, Yao BIB006 proposes to transfer knowledge from multiple source domains using a boosting method in an attempt to minimize the effects of negative transfer from a single unrelated source domain. The boosting process requires some amount of labeled target data. Yao BIB006 effectively extends the work of Dai BIB002 (TrAdaBoost) by expanding the transfer boosting algorithm to multiple source domains. In the TrAdaBoost algorithm, during every boosting iteration, a so-called weak classifier is built using weighted instance data from the previous iteration. Then, the misclassified source instances are lowered in importance and the misclassified target instances are raised in importance. In the multi-source TrAdaBoost algorithm (called MsTrAdaBoost), each iteration step first finds a weak classifier for each source and target combination, and then the final weak classifier is selected for that iteration by finding the one that minimizes the target classification error. The instance reweighting step remains the same as in the TrAdaBoost. An alternative multi-source boosting method (TaskTrAdaBoost) is proposed that transfers internal learner parameter information from the source to the target. The TaskTrAdaBoost algorithm first finds candidate weak classifiers from each individual source by performing an AdaBoost process on each source domain. Then an AdaBoost process is performed on the labeled target data, and at every boosting iteration, the weak classifier used is selected from the candidate weak source classifiers (found in the previous step) that has the lowest classification error using the labeled target data. Experiments are performed for the application of object category recognition where the area under the curve (AUC) is measured as the performance metric. An AdaBoost baseline approach using only the limited labeled target data is measured along with a TrAdaBoost approach using a single source (the multiple sources are combined to one) and the limited labeled target data. Linear SVM learners are used as the base classifiers in all approaches. Both the MsTrAdaBoost and TaskTrAdaBoost approaches outperform the baseline approach and TrAdaBoost approach. The MsTrAdaBoost and TaskTrAdaBoost demonstrated similar performance.