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A Survey on Transfer Learning
"... 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 realworld applications, this assumption may not hold. For example, we sometimes have a classification task i ..."
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Cited by 446 (23 self)
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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 realworld 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.
A framework for learning predictive structures from multiple tasks and unlabeled data
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semisupervised learning. Although a number of such methods ar ..."
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Cited by 437 (3 self)
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One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semisupervised learning. Although a number of such methods are proposed, at the current stage, we still don’t have a complete understanding of their effectiveness. This paper investigates a closely related problem, which leads to a novel approach to semisupervised learning. Specifically we consider learning predictive structures on hypothesis spaces (that is, what kind of classifiers have good predictive power) from multiple learning tasks. We present a general framework in which the structural learning problem can be formulated and analyzed theoretically, and relate it to learning with unlabeled data. Under this framework, algorithms for structural learning will be proposed, and computational issues will be investigated. Experiments will be given to demonstrate the effectiveness of the proposed algorithms in the semisupervised learning setting.
Regularized multitask learning
, 2004
"... This paper provides a foundation for multi–task learning using reproducing kernel Hilbert spaces of vector–valued functions. In this setting, the kernel is a matrix–valued function. Some explicit examples will be described which go beyond our earlier results in [7]. In particular, we characterize cl ..."
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Cited by 267 (2 self)
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This paper provides a foundation for multi–task learning using reproducing kernel Hilbert spaces of vector–valued functions. In this setting, the kernel is a matrix–valued function. Some explicit examples will be described which go beyond our earlier results in [7]. In particular, we characterize classes of matrix– valued kernels which are linear and are of the dot product or the translation invariant type. We discuss how these kernels can be used to model relations between the tasks and present linear multi–task learning algorithms. Finally, we present a novel proof of the representer theorem for a minimizer of a regularization functional which is based on the notion of minimal norm interpolation. 1
Convex multitask feature learning
 MACHINE LEARNING
, 2007
"... We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the wellknown singletask 1norm regularization. It is based on a novel nonconvex regularizer which controls the number of learned features common across the tasks. We prove th ..."
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Cited by 252 (25 self)
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We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the wellknown singletask 1norm regularization. It is based on a novel nonconvex regularizer which controls the number of learned features common across the tasks. We prove that the method is equivalent to solving a convex optimization problem for which there is an iterative algorithm which converges to an optimal solution. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the former step it learns taskspecific functions and in the latter step it learns commonacrosstasks sparse representations for these functions. We also provide an extension of the algorithm which learns sparse nonlinear representations using kernels. We report experiments on simulated and real data sets which demonstrate that the proposed method can both improve the performance relative to learning each task independently and lead to a few learned features common across related tasks. Our algorithm can also be used, as a special case, to simply select – not learn – a few common variables across the tasks.
Learning Multiple Tasks with Kernel Methods
 Journal of Machine Learning Research
, 2005
"... Editor: John ShaweTaylor We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard singletask kernel methods, such as support vector machines and regularization networks, are extended to the case of multitask learning. Our analysis sh ..."
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Cited by 250 (10 self)
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Editor: John ShaweTaylor We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard singletask kernel methods, such as support vector machines and regularization networks, are extended to the case of multitask learning. Our analysis shows that the problem of estimating many task functions with regularization can be cast as a single task learning problem if a family of multitask kernel functions we define is used. These kernels model relations among the tasks and are derived from a novel form of regularizers. Specific kernels that can be used for multitask learning are provided and experimentally tested on two real data sets. In agreement with past empirical work on multitask learning, the experiments show that learning multiple related tasks simultaneously using the proposed approach can significantly outperform standard singletask learning particularly when there are many related tasks but few data per task.
Multitask feature learning
 Advances in Neural Information Processing Systems 19
, 2007
"... We present a method for learning a lowdimensional representation which is shared across a set of multiple related tasks. The method builds upon the wellknown 1norm regularization problem using a new regularizer which controls the number of learned features common for all the tasks. We show that th ..."
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Cited by 234 (8 self)
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We present a method for learning a lowdimensional representation which is shared across a set of multiple related tasks. The method builds upon the wellknown 1norm regularization problem using a new regularizer which controls the number of learned features common for all the tasks. We show that this problem is equivalent to a convex optimization problem and develop an iterative algorithm for solving it. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the latter step we learn commonacrosstasks representations and in the former step we learn taskspecific functions using these representations. We report experiments on a simulated and a real data set which demonstrate that the proposed method dramatically improves the performance relative to learning each task independently. Our algorithm can also be used, as a special case, to simply select – not learn – a few common features across the tasks.
Analysis of representations for domain adaptation
 In NIPS
, 2007
"... Domain is a distribution D on an instance set X Domain adaptation of a classifier A classification task Source domain (DS) ..."
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Cited by 162 (11 self)
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Domain is a distribution D on an instance set X Domain adaptation of a classifier A classification task Source domain (DS)
Boosting for transfer learning
 In Proceedings of the 24th International Conference on Machine Learning, ICML ’07
, 2007
"... Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identicaldistribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled ..."
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Cited by 159 (12 self)
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Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identicaldistribution 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 boostingbased 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 highquality 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.
Constructing informative priors using transfer learning
 In Proceedings of the 23rd International Conference on Machine Learning
, 2006
"... Many applications of supervised learning require good generalization from limited labeled data. In the Bayesian setting, we can try to achieve this goal by using an informative prior over the parameters, one that encodes useful domain knowledge. Focusing on logistic regression, we present an algorit ..."
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Cited by 112 (0 self)
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Many applications of supervised learning require good generalization from limited labeled data. In the Bayesian setting, we can try to achieve this goal by using an informative prior over the parameters, one that encodes useful domain knowledge. Focusing on logistic regression, we present an algorithm for automatically constructing a multivariate Gaussian prior with a full covariance matrix for a given supervised learning task. This prior relaxes a commonly used but overly simplistic independence assumption, and allows parameters to be dependent. The algorithm uses other “similar ” learning problems to estimate the covariance of pairs of individual parameters. We then use a semidefinite program to combine these estimates and learn a good prior for the current learning task. We apply our methods to binary text classification, and demonstrate a 20 to 40% test error reduction over a commonly used prior. 1.
MultiTask Feature and Kernel Selection for SVMs
 Proc. of ICML 2004
, 2004
"... We compute a common feature selection or kernel selection con guration for multiple support vector machines (SVMs) trained on dierent yet interrelated datasets. The method is advantageous when multiple classi cation tasks and dierently labeled datasets exist over a common input space. Diere ..."
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Cited by 84 (3 self)
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We compute a common feature selection or kernel selection con guration for multiple support vector machines (SVMs) trained on dierent yet interrelated datasets. The method is advantageous when multiple classi cation tasks and dierently labeled datasets exist over a common input space. Dierent datasets can mutually reinforce a common choice of representation or relevant features for their various classi ers. We derive a multitask representation learning approach using the maximum entropy discrimination formalism. The resulting convex algorithms maintain the global solution properties of support vector machines. However, in addition to multiple SVM classi cation /regression parameters they also jointly estimate an optimal subset of features or optimal combination of kernels. Experiments are shown on standardized datasets.