Query by Transduction
There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT qu...
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| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence Jg. 30; H. 9; S. 1557 - 1571 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Los Alamitos, CA
IEEE
01.09.2008
IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0162-8828, 1939-3539 |
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| Abstract | There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT queries the label of an example based on the p-values obtained using transduction. We show that QBT is closely related to Query-by-Committee (QBC) using relations between transduction, Bayesian statistical testing, Kullback-Leibler divergence, and Shannon information. The feasibility and utility of QBT is shown on both binary and multi-class classification tasks using SVM as the choice classifier. Our experimental results show that QBT compares favorably, in terms of mean generalization, against random sampling, committee-based active learning, margin-based active learning, and QBC in the stream-based setting. |
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| AbstractList | There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT queries the label of an example based on the p-values obtained using transduction. We show that QBT is closely related to Query-by-Committee (QBC) using relations between transduction, Bayesian statistical testing, Kullback-Leibler divergence, and Shannon information. The feasibility and utility of QBT is shown on both binary and multi-class classification tasks using SVM as the choice classifier. Our experimental results show that QBT compares favorably, in terms of mean generalization, against random sampling, committee-based active learning, margin-based active learning, and QBC in the stream-based setting.There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT queries the label of an example based on the p-values obtained using transduction. We show that QBT is closely related to Query-by-Committee (QBC) using relations between transduction, Bayesian statistical testing, Kullback-Leibler divergence, and Shannon information. The feasibility and utility of QBT is shown on both binary and multi-class classification tasks using SVM as the choice classifier. Our experimental results show that QBT compares favorably, in terms of mean generalization, against random sampling, committee-based active learning, margin-based active learning, and QBC in the stream-based setting. There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction (QBT) as a novel active learning algorithm. QBT queries the label of an example based on the p-values obtained using transduction. We show that QBT is closely related to Query-by-Committee (QBC) using relations between transduction, Bayesian statistical testing, Kullback-Leibler divergence, and Shannon information. The feasibility and utility of QBT is shown on both binary and multi-class classification tasks using SVM as the choice classifier. Our experimental results show that QBT compares favorably, in terms of mean generalization, against random sampling, committee-based active learning, margin-based active learning, and QBC in the stream-based setting. There has been recently a growing interest in the use of transductive inference for learning. We expand here the scope of transductive inference to active learning in a stream-based setting. Towards that end this paper proposes Query-by-Transduction [abstract truncated by publisher]. |
| Author | Shen-Shyang Ho Wechsler, H. |
| Author_xml | – sequence: 1 surname: Shen-Shyang Ho fullname: Shen-Shyang Ho organization: NASA jet Propulsion Lab., Pasadena, CA – sequence: 2 givenname: H. surname: Wechsler fullname: Wechsler, H. |
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| Keywords | Statistical Machine learning Bayes estimation Statistical analysis Divergence Random sampling Database query hypothesis testing Active learning Inference Shannon theory transductive inference Hypothesis test Statistical test support vector machine Active system Kolmogorov complexity Vector support machine Feasibility Learning algorithm Pattern analysis Kolmogorov equation Artificial intelligence Multiclass |
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| References | Baram (ref43) 2004 ref12 ref56 ref15 Graepel (ref57) ref59 ref14 ref53 ref11 ref17 ref16 ref18 Zhu (ref19) Tong (ref6) 2001; 2 Vovk (ref49) ref51 ref46 ref45 ref47 ref42 ref41 Kullback (ref10) 1959 ref44 Weerahandi (ref50) 1994 Abe (ref26) Roy (ref20) Yan (ref40) ref8 Schohn (ref22) ref7 ref9 Cauwenberghs (ref54) 2000 ref4 Luo (ref37) 2005; 6 ref3 ref5 ref35 ref34 ref36 ref31 Gilad-Bachrach (ref30) Saunders (ref52) ref33 Zhang (ref13) ref1 ref39 ref38 Campbell (ref23) Joachims (ref2) Ho (ref48) Brinker (ref24) 2004 ref25 Graepel (ref58) ref21 ref28 ref27 ref29 ref60 McCallum (ref32) ref61 Ho (ref55) |
| References_xml | – ident: ref36 doi: 10.1145/500141.500159 – start-page: 514 volume-title: Proc. Ann. Conf. Advances in Neural Information Processing Systems (NIPS ’00) ident: ref58 article-title: The Kernel Gibbs Sampler – ident: ref4 doi: 10.3115/1220575.1220696 – ident: ref27 doi: 10.1016/B978-1-55860-377-6.50027-X – ident: ref39 doi: 10.15607/RSS.2005.I.002 – start-page: 67 volume-title: Proc. IEEE Int’l Conf. Multimedia and Expo ident: ref40 article-title: Multi-Class Active Learning for Video Semantic Feature Extraction – volume: 6 start-page: 589 year: 2005 ident: ref37 article-title: Active Learning to Recognize Multiple Types of Plankton publication-title: J. Machine Learning Research – start-page: 1191 volume-title: Proc. 17th Int’l Conf. Machine Learning ident: ref13 article-title: A Probability Analysis on the Value of Unlabeled Data for Classification Problems – ident: ref35 doi: 10.1145/1027527.1027664 – start-page: 441 volume-title: Proc. 18th Int’l Conf. Machine Learning ident: ref20 article-title: Toward Optimal Active Learning through Sampling Estimation of Error Reduction – ident: ref29 doi: 10.1145/1015330.1015385 – start-page: 409 volume-title: Advances in Neural Information Processing Systems 13 year: 2000 ident: ref54 article-title: Incremental Support Vector Machine Learning – ident: ref15 doi: 10.1016/B978-1-55860-335-6.50026-X – ident: ref56 doi: 10.1198/000313001300339950 – ident: ref7 doi: 10.1007/b106715 – ident: ref34 doi: 10.1109/ICCV.2003.1238391 – ident: ref47 doi: 10.1145/1143844.1143980 – ident: ref59 doi: 10.1162/neco.1989.1.4.541 – volume-title: Proc. ICDM Workshop Temporal Data Mining: Algorithms, Theory and Applications (TDM ’04) ident: ref55 article-title: Learning from Data Streams via Online Transduction – ident: ref17 doi: 10.1109/TMM.2002.1017738 – start-page: 200 volume-title: Proc. 16th Int’l Conf. Machine Learning ident: ref2 article-title: Transductive Inference for Text Classification Using Support Vector Machines – start-page: 359 volume-title: Proc. 15th Int’l Conf. Machine Learning ident: ref32 article-title: Employing EM and Pool-Based Active Learning for Text Classification – volume-title: Proc. ICML Workshop Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining ident: ref19 article-title: Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions – ident: ref38 doi: 10.1021/ci025620t – ident: ref8 doi: 10.1007/978-1-4757-2606-0 – ident: ref46 doi: 10.7551/mitpress/9780262033589.001.0001 – start-page: 456 volume-title: Proc. Ann. Conf. Advances in Neural Information Processing Systems (NIPS ’99) ident: ref57 article-title: Bayesian Transduction – ident: ref44 doi: 10.1109/TNN.2003.820446 – ident: ref18 doi: 10.1145/130385.130417 – ident: ref51 doi: 10.1007/3-540-36755-1_32 – ident: ref41 doi: 10.1145/1143844.1143897 – ident: ref3 doi: 10.1109/TPAMI.2005.224 – ident: ref42 doi: 10.1145/1135777.1135870 – ident: ref45 doi: 10.1002/9780470140529 – volume-title: Proc. Int’l Joint Conf. Neural Network (IJCNN ’03) ident: ref48 article-title: Transductive Confidence Machines for Active Learning – volume-title: Exact Statistical Methods for Data Analysis year: 1994 ident: ref50 – volume-title: Information Theory and Statistics year: 1959 ident: ref10 – ident: ref1 doi: 10.1007/978-1-4757-3264-1 – ident: ref11 doi: 10.1023/A:1007330508534 – start-page: 839 volume-title: Proc. 17th Int’l Conf. Machine Learning ident: ref22 article-title: Less Is More: Active Learning with Support Vector Machines – ident: ref5 doi: 10.1145/1141277.1141314 – volume-title: Proc. Ann. Conf. Advances in Neural Information Processing Systems (NIPS ’05) ident: ref30 article-title: Query by Committee Made Real – start-page: 111 volume-title: Proc. 17th Int’l Conf. Machine Learning ident: ref23 article-title: Query Learning with Large Margin Classifiers – ident: ref21 doi: 10.1109/ICCV.2003.1238391 – start-page: 444 volume-title: Proc. 16th Int’l Conf. Machine Learning ident: ref49 article-title: Machine-Learning Applications of Algorithmic Randomness – ident: ref25 doi: 10.1109/TPAMI.2004.1262340 – ident: ref16 doi: 10.1162/neco.1992.4.4.590 – ident: ref28 doi: 10.1109/ICASSP.2003.1198771 – ident: ref31 doi: 10.1007/11503415_17 – volume-title: PhD dissertation year: 2004 ident: ref24 article-title: Active Learning with Kernel Machines – start-page: 722 volume-title: Proc. 16th Int’l Joint Conf. Artificial Intelligence ident: ref52 article-title: Transduction with Confidence and Credibility – ident: ref53 doi: 10.1007/3-540-44795-4_31 – ident: ref9 doi: 10.1016/S0304-3975(02)00100-7 – volume: 2 start-page: 45 year: 2001 ident: ref6 article-title: Support Vector Machine Active Learning with Applications to Text Classification publication-title: J. Machine Learning Research – start-page: 1 volume-title: Proc. 15th Int’l Conf. Machine Learning ident: ref26 article-title: Query Learning Strategies Using Boosting and Bagging – start-page: 255 year: 2004 ident: ref43 article-title: Online Choice of Active Learning Algorithms publication-title: J. Machine Learning Research – ident: ref60 doi: 10.1023/A:1007618119488 – ident: ref12 doi: 10.1613/jair.295 – ident: ref61 doi: 10.1023/A:1022606404104 – ident: ref14 doi: 10.1109/TPAMI.2006.156 – ident: ref33 doi: 10.1007/11564096_28 |
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| SubjectTerms | Algorithms Applied sciences Artificial Intelligence Bayesian methods Classification Computer science; control theory; systems Data processing. List processing. Character string processing Exact sciences and technology Feasibility Frequency Inference Inference algorithms Information Storage and Retrieval - methods Information systems. Data bases Labeling Learning Machine learning Mathematics Memory organisation. Data processing Pattern analysis Pattern Recognition, Automated - methods Probability and statistics Query processing Sampling methods Sampling theory, sample surveys Sciences and techniques of general use Sensitivity and Specificity Software Statistical Statistical analysis Statistics Support vector machine classification Support vector machines Tasks Testing Utilities |
| Title | Query by Transduction |
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