Boosting for transfer learning

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.

[1]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[2]  J. Heckman.  Sample selection bias as a specification error , 1979 .

[3]  W. Greene.  Sample Selection Bias as a Specification Error: Comment , 1981 .

[4]  David Haussler,et al.  Proceedings of the fifth annual workshop on Computational learning theory , 1992, COLT 1992.

[5]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[6]  Juergen Schmidhuber,et al.  On learning how to learn learning strategies , 1994 .

[7]  Sebastian Thrun,et al.  Learning One More Thing , 1994, IJCAI.

[8]  urgen Schmidhuber.  On Learning How to Learn Learning Strategies Technical Report Fki-198-94 (revised) , 1995 .

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[10]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[11]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[12]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[13]  Rich Caruana,et al.  Multitask Learning , 1997, Machine-mediated learning.

[14]  Robert E. Schapire,et al.  A Brief Introduction to Boosting , 1999, IJCAI.

[15]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[16]  H. Shimodaira,et al.  Improving predictive inference under covariate shift by weighting the log-likelihood function , 2000 .

[17]  Thorsten Joachims,et al.  Learning to classify text using support vector machines - methods, theory and algorithms , 2002, The Kluwer international series in engineering and computer science.

[18]  T. Ben-David,et al.  Exploiting Task Relatedness for Multiple , 2003 .

[19]  Andrew McCallum,et al.  Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..

[20]  Thomas G. Dietterich,et al.  Improving SVM accuracy by training on auxiliary data sources , 2004, ICML.

[21]  Bianca Zadrozny,et al.  Learning and evaluating classifiers under sample selection bias , 2004, ICML.

[22]  Lawrence Carin,et al.  Logistic regression with an auxiliary data source , 2005, ICML.

[23]  Miroslav Dudík,et al.  Correcting sample selection bias in maximum entropy density estimation , 2005, NIPS.

[24]  Thomas G. Dietterich,et al.  To transfer or not to transfer , 2005, NIPS 2005.

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

[26]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[27]  Steffen Bickel,et al.  Dirichlet-Enhanced Spam Filtering based on Biased Samples , 2006, NIPS.