A survey on heterogeneous transfer learning <s> COTL, OHTWC <s> We introduce and analyze a new algorithm for linear classification which combines Rosenblatt's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large margins. Compared to Vapnik's algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximal-margin classifiers on the same problem, while saving significantly on computation time and programming effort. <s> BIB001 </s> A survey on heterogeneous transfer learning <s> COTL, OHTWC <s> We present a unified view for online classification, regression, and uni-class problems. This view leads to a single algorithmic framework for the three problems. We prove worst case loss bounds for various algorithms for both the realizable case and the non-realizable case. A conversion of our main online algorithm to the setting of batch learning is also discussed. The end result is new algorithms and accompanying loss bounds for the hinge-loss. <s> BIB002 </s> A survey on heterogeneous transfer learning <s> COTL, OHTWC <s> Online learning is the process of answering a sequence of questions given knowledge of the correct answers to previous questions and possibly additional available information. Answering questions in an intelligent fashion and being able to make rational decisions as a result is a basic feature of everyday life. Will it rain today (so should I take an umbrella)? Should I fight the wild animal that is after me, or should I run away? Should I open an attachment in an email message or is it a virus? The study of online learning algorithms is thus an important domain in machine learning, and one that has interesting theoretical properties and practical applications. This dissertation describes a novel framework for the design and analysis of online learning algorithms. We show that various online learning algorithms can all be derived as special cases of our algorithmic framework. This unified view explains the properties of existing algorithms and also enables us to derive several new interesting algorithms. Online learning is performed in a sequence of consecutive rounds, where at each round the learner is given a question and is required to provide an answer to this question. After predicting an answer, the correct answer is revealed and the learner suffers a loss if there is a discrepancy between his answer and the correct one. The algorithmic framework for online learning we propose in this dissertation stems from a connection that we make between the notions of regret in online learning and weak duality in convex optimization. Regret bounds are the common thread in the analysis of online learning algorithms. A regret bound measures the performance of an online algorithm relative to the performance of a competing prediction mechanism, called a competing hypothesis. The competing hypothesis can be chosen in hindsight from a class of hypotheses, after observing the entire sequence of question- answer pairs. Over the years, competitive analysis techniques have been refined and extended to numerous prediction problems by employing complex and varied notions of progress toward a good competing hypothesis. We propose a new perspective on regret bounds which is based on the notion of duality in convex optimization. Regret bounds are universal in the sense that they hold for any possible fixed hypothesis in a given hypothesis class. We therefore cast the universal bound as a lower bound <s> BIB003 </s> A survey on heterogeneous transfer learning <s> COTL, OHTWC <s> In this paper, we investigate a new machine learning framework called Online Transfer Learning (OTL) that aims to transfer knowledge from some source domain to an online learning task on a target domain. We do not assume the target data follows the same class or generative distribution as the source data, and our key motivation is to improve a supervised online learning task in a target domain by exploiting the knowledge that had been learned from large amount of training data in source domains. OTL is in general challenging since data in both domains not only can be different in their class distributions but can be also different in their feature representations. As a first attempt to this problem, we propose techniques to address two kinds of OTL tasks: one is to perform OTL in a homogeneous domain, and the other is to perform OTL across heterogeneous domains. We show the mistake bounds of the proposed OTL algorithms, and empirically examine their performance on several challenging OTL tasks. Encouraging results validate the efficacy of our techniques. <s> BIB004 </s> A survey on heterogeneous transfer learning <s> COTL, OHTWC <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> BIB005 </s> A survey on heterogeneous transfer learning <s> COTL, OHTWC <s> In this paper, we study online heterogeneous transfer learning (HTL) problems where offline labeled data from a source domain is transferred to enhance the online classification performance in a target domain. The main idea of our proposed algorithm is to build an offline classifier based on heterogeneous similarity constructed by using labeled data from a source domain and unlabeled co-occurrence data which can be easily collected from web pages and social networks. We also construct an online classifier based on data from a target domain, and combine the offline and online classifiers by using the Hedge weighting strategy to update their weights for ensemble prediction. The theoretical analysis of error bound of the proposed algorithm is provided. Experiments on a real-world data set demonstrate the effectiveness of the proposed algorithm. <s> BIB006
Zhao et al. BIB004 proposed a framework called Online Transfer Learning (OTL) to address online transfer learning tasks under homogeneous and heterogeneous scenarios. Specifically, the heterogeneous approach is called Co-regularized OTL (COTL). Standard machine learning assumes that training data is provided all at once in a batch manner, but this assumption may not always hold in real-world applications. Online learning BIB003 tasks are designed for when training instances arrive in an online/sequential manner. Online transfer learning aims to transfer knowledge from an offline source domain to an online target learning task which is represented by a different feature space. During these online HTL tasks, OTL assumes that the feature space of the source is a subset of the target domain and the procedure to solve these tasks is as follows. First, a classifier is built from the labeled source data, denoted as h(x), using regular supervised learning with SVM or online learning via the Perceptron algorithm  BIB001 . The method then proposes to use a multi-view approach for the target data. Multi-view  theory describes how the same data can be viewed through different representations or "views". Two classifiers are built for two newly constructed views for the target data (denoted as f 1 and f 2 ) where f 1 is initialized to the source prediction function h and f 2 is initialized to 0. The predicted label is calculated as a function of these two views thus creating an ensemble classifier. For each new arriving training instance, both functions are updated using co-regularization optimization. If an arriving target training instance is incorrectly predicted then the ensemble is updated through the optimization procedure which aims to classify the next new example correctly without deviating too much from the original ensemble through regularization terms. This method thus exploits source domain knowledge and enhances an online learning target task having both domains represented by differing feature spaces. Experiments were performed comparing the proposed COTL against the Passive Aggressive algorithm (PA) BIB002 , PA initialized to the source, and COTL with both views initialized to 0. The proposed COTL demonstrated superior performance by producing lower mistake rates, i.e. higher accuracy, on benchmark datasets which proves the method as an effective technique for knowledge transfer for online learning tasks. Yan et al. BIB006 also proposed a method for online HTL learning tasks called Online Heterogeneous Transfer with Weighted Classifiers (OHTWC). This method proposes using unlabeled co-occurrence data to serve as a bridge between the two domains. The way this method works is by first creating an offline source classifier based on the similarity relationship between the instances of the source and target through the co-occurrence data. Using Pearson correlation  , the similarity is measured between the new arriving target instance and its co-occurred counterpart. Then, the similarity between the source and its co-occurred counterpart is measured. Therefore, combining these two measures will provide us with the similarity of the arriving target instance with the source from a different feature space. The prediction for this classifier is done through a weighted sum of the k-nearest neighbors of the source instances deemed most similar. A second classifier is then constructed for the target instances using the online Passive Aggressive BIB002 algorithm. This classifier is updated for each arriving training instance based on whether it predicted the instance correctly or not. The updating is done through a positive regularization parameter and a hinge loss function. Having these two classifiers, the final prediction for arriving instances is done as an ensemble of the two. A Hedge  strategy is used to dynamically update weights assigned to each of the classifiers. This is done by changing the voting weights based on the loss suffered from each classifier respectively. The larger the loss, the larger the weight decrease occurs to such classifier causing the more accurate classifier to have a higher weight for prediction purposes. Figure 2 , adapted from BIB006 , provides an illustration of this method for using weighted online and offline classifiers to perform an online task. An experiment for textto-image classification was performed to test the effectiveness of the proposed algorithm. The proposed algorithm was compared against PA BIB002 , SVM, HTLIC BIB005 , and PA using k-NN on co-occurrence data. The results show that the proposed OHTWC algorithm achieved best performance against the baselines for most cases by having the lowest error rates. OHTWC differs from COTL in the manner that OHTWC uses a weighted ensemble rather than a co-regularized multi-view approach. It also uses co-occurrence data to link the source and target domains while COTL does not since COTL only uses the source and target data.