Online Transfer Learning

In this paper, we propose a novel machine learning framework called "Online Transfer Learning" (OTL), which aims to attack an online learning task on a target domain by transferring knowledge from some source domain. We do not assume data in the target domain follows the same distribution as that in the source domain, and the motivation of our work is to enhance a supervised online learning task on a target domain by exploiting the existing knowledge that had been learnt from training data in source domains. OTL is in general very challenging since data in both source and target domains not only can be different in their class distributions, but also can be diverse in their feature representations. As a first attempt to this new research problem, we investigate two different settings of OTL: (i) OTL on homogeneous domains of common feature space, and (ii) OTL across heterogeneous domains of different feature spaces. For each setting, we propose effective OTL algorithms to solve online classification tasks, and show some theoretical bounds of the algorithms. In addition, we also apply the OTL technique to attack the challenging online learning tasks with concept-drifting data streams. Finally, we conduct extensive empirical studies on a comprehensive testbed, in which encouraging results validate the efficacy of our techniques.

[1]  Mark Herbster,et al.  Tracking the Best Linear Predictor , 2001, J. Mach. Learn. Res..

[2]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[3]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[4]  Indre Zliobaite,et al.  Learning under Concept Drift: an Overview , 2010, ArXiv.

[5]  Kenneth D. Forbus,et al.  Analogical model formulation for transfer learning in AP Physics , 2009, Artif. Intell..

[6]  Steven C. H. Hoi,et al.  LIBOL: a library for online learning algorithms , 2014, J. Mach. Learn. Res..

[7]  Daniel Marcu,et al.  Domain Adaptation for Statistical Classifiers , 2006, J. Artif. Intell. Res..

[8]  Zenglin Xu,et al.  Online Learning for Group Lasso , 2010, ICML.

[9]  Yi Li,et al.  The Relaxed Online Maximum Margin Algorithm , 1999, Machine Learning.

[10]  Massimiliano Pontil,et al.  An Algorithm for Transfer Learning in a Heterogeneous Environment , 2008, ECML/PKDD.

[11]  J. Michaelsen Cross-Validation in Statistical Climate Forecast Models , 1987 .

[12]  Steven C. H. Hoi,et al.  Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning , 2012, ICML.

[13]  Claudio Gentile,et al.  Tracking the best hyperplane with a simple budget Perceptron , 2006, Machine Learning.

[14]  Gábor Lugosi,et al.  Prediction, learning, and games , 2006 .

[15]  Rong Jin,et al.  Online AUC Maximization , 2011, ICML.

[16]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[17]  Thorsten Joachims,et al.  Detecting Concept Drift with Support Vector Machines , 2000, ICML.

[18]  Shai Shalev-Shwartz,et al.  Online learning: theory, algorithms and applications (למידה מקוונת.) , 2007 .

[19]  Ralf Klinkenberg,et al.  Learning drifting concepts: Example selection vs. example weighting , 2004, Intell. Data Anal..

[20]  Vikas Sindhwani,et al.  An RKHS for multi-view learning and manifold co-regularization , 2008, ICML '08.

[21]  Philip M. Long,et al.  Tracking drifting concepts by minimizing disagreements , 2004, Machine Learning.

[22]  Ramesh Nallapati,et al.  A Comparative Study of Methods for Transductive Transfer Learning , 2007, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007).

[23]  Qiang Yang,et al.  Self-taught clustering , 2008, ICML '08.

[24]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[25]  Shonali Krishnaswamy,et al.  Mining data streams: a review , 2005, SGMD.

[26]  Philip M. Long,et al.  Online Learning of Multiple Tasks with a Shared Loss , 2007, J. Mach. Learn. Res..

[27]  Rich Caruana,et al.  Inductive Transfer for Bayesian Network Structure Learning , 2007, ICML Unsupervised and Transfer Learning.

[28]  Steven C. H. Hoi,et al.  Exact Soft Confidence-Weighted Learning , 2012, ICML.

[29]  Rong Jin,et al.  Double Updating Online Learning , 2011, J. Mach. Learn. Res..

[30]  Yoav Freund,et al.  Large Margin Classification Using the Perceptron Algorithm , 1998, COLT.

[31]  Georgios Paliouras,et al.  Filtron: A Learning-Based Anti-Spam Filter , 2004, CEAS.

[32]  Alan M. Frieze,et al.  A Polynomial-Time Algorithm for Learning Noisy Linear Threshold Functions , 1996, Algorithmica.

[33]  Philip M. Long,et al.  Tracking Drifting Concepts By Minimizing Disagreements , 2004, Machine Learning.

[34]  Changshui Zhang,et al.  Transferred Dimensionality Reduction , 2008, ECML/PKDD.

[35]  Rong Jin,et al.  DUOL: A Double Updating Approach for Online Learning , 2009, NIPS.

[36]  Koby Crammer,et al.  Online Passive-Aggressive Algorithms , 2003, J. Mach. Learn. Res..

[37]  Seshadhri Comandur,et al.  Efficient learning algorithms for changing environments , 2009, ICML '09.

[38]  Claudio Gentile,et al.  On the generalization ability of on-line learning algorithms , 2001, IEEE Transactions on Information Theory.

[39]  Yoav Freund,et al.  Large Margin Classification Using the Perceptron Algorithm , 1998, COLT' 98.

[40]  Steven C. H. Hoi,et al.  OTL: A Framework of Online Transfer Learning , 2010, ICML.

[41]  Peter L. Bartlett,et al.  Learning with a slowly changing distribution , 1992, COLT '92.