Effective Active Learning Strategies for the Use of Large-Margin Classifiers in Semantic Annotation: An Optimal Parameter Discovery Perspective
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Raymond Y. K. Lau | Stephen Shaoyi Liao | J. Leon Zhao | Kaiquan Xu | J. Zhao | S. Liao | Kaiquan Xu
[1] Ming-Syan Chen,et al. Selective data acquisition for probabilistic K-NN query , 2010, CIKM '10.
[2] Diana Maynard,et al. Metrics for Evaluation of Ontology-based Information Extraction , 2006, EON@WWW.
[3] Vladimir Cherkassky,et al. The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.
[4] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[5] Siegfried Handschuh,et al. Semantic annotation for knowledge management: Requirements and a survey of the state of the art , 2006, J. Web Semant..
[6] Nello Cristianini,et al. Further results on the margin distribution , 1999, COLT '99.
[7] Mike Y. Chen,et al. Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web , 2001 .
[8] Nagiza F. Samatova,et al. Multi-Criterion Active Learning in Conditional Random Fields , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).
[9] Burr Settles,et al. From Theories to Queries: Active Learning in Practice , 2011 .
[10] Jeff A. Bilmes,et al. Active Learning as Non-Convex Optimization , 2009, AISTATS.
[11] Marti A. Hearst. Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..
[12] Xiaoli Zhang,et al. A Structural SVM Approach for Reference Parsing , 2010, ICMLA.
[13] Thomas Hofmann,et al. Support vector machine learning for interdependent and structured output spaces , 2004, ICML.
[14] Mark Craven,et al. An Analysis of Active Learning Strategies for Sequence Labeling Tasks , 2008, EMNLP.
[15] Ali F. Farhoomand,et al. Managerial information overload , 2002, CACM.
[16] Tomaso A. Poggio,et al. Statistical Learning Theory: A Primer , 2000, International Journal of Computer Vision.
[17] Daniel Jurafsky,et al. Support Vector Learning for Semantic Argument Classification , 2005, Machine Learning.
[18] Bo Zhang,et al. Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction , 2008, J. Mach. Learn. Res..
[19] Andrew McCallum,et al. Reducing Labeling Effort for Structured Prediction Tasks , 2005, AAAI.
[20] Larry Reeve,et al. Integrating Hidden Markov Models Into Semantic Web Annotation Platforms , 2004 .
[21] Q. Henry Wu,et al. Online training of support vector classifier , 2003, Pattern Recognit..
[22] Stefan Wrobel,et al. Active Hidden Markov Models for Information Extraction , 2001, IDA.
[23] Thorsten Joachims,et al. Making large-scale support vector machine learning practical , 1999 .
[24] Foster J. Provost,et al. Active Feature-Value Acquisition , 2009, Manag. Sci..
[25] Andrew McCallum,et al. Piecewise pseudolikelihood for efficient training of conditional random fields , 2007, ICML '07.
[26] Raymond Y. K. Lau,et al. Towards a belief-revision-based adaptive and context-sensitive information retrieval system , 2008, TOIS.
[27] W. Scott Spangler,et al. Generating and Browsing Multiple Taxonomies Over a Document Collection , 2003, J. Manag. Inf. Syst..
[28] Jaideep Srivastava,et al. Web Business Intelligence: Mining the Web for Actionable Knowledge , 2003, INFORMS J. Comput..
[29] Bin Zhao,et al. Max margin learning on domain-independent web information extraction , 2011, CIKM '11.
[30] Zhiqiang Zheng,et al. Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution , 2006, Manag. Sci..
[31] Andrew R. Webb,et al. Statistical Pattern Recognition , 1999 .
[32] Michael W. Berry,et al. Survey of Text Mining: Clustering, Classification, and Retrieval , 2007 .
[33] Yagang Zhang,et al. Application of Machine Learning , 2010 .
[34] Burr Settles,et al. Active Learning Literature Survey , 2009 .
[35] Stéphane Canu,et al. Benchmarking of semantic annotation with conditional random fields , 2005 .
[36] Hyoil Han,et al. Survey of semantic annotation platforms , 2005, SAC '05.
[37] V Korde,et al. TEXT CLASSIFICATION AND CLASSIFIERS: A SURVEY , 2012 .
[38] Zahir Tari,et al. On the Move to Meaningful Internet Systems 2006: CoopIS, DOA, GADA, and ODBASE, OTM Confederated International Conferences, CoopIS, DOA, GADA, and ODBASE 2006, Montpellier, France, October 29 - November 3, 2006. Proceedings, Part I , 2006, OTM Conferences.
[39] Erik F. Tjong Kim Sang,et al. Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition , 2003, CoNLL.
[40] Tom M. Mitchell,et al. Generalization as Search , 2002 .
[41] Rong Yan,et al. Automatically labeling video data using multi-class active learning , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[42] Weiguo Fan,et al. Genetic Programming-Based Discovery of Ranking Functions for Effective Web Search , 2005, J. Manag. Inf. Syst..
[43] Christoph H. Lampert,et al. Active Structured Learning for High-Speed Object Detection , 2009, DAGM-Symposium.
[44] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[45] Andrew McCallum,et al. Toward Optimal Active Learning through Sampling Estimation of Error Reduction , 2001, ICML.
[46] Erik F. Tjong Kim Sang,et al. Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition , 2002, CoNLL.
[47] Gideon S. Mann,et al. Learning from labeled features using generalized expectation criteria , 2008, SIGIR '08.
[48] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[49] Jian Su,et al. Multi-Criteria-based Active Learning for Named Entity Recognition , 2004, ACL.
[50] Tom Heskes,et al. Efficiently learning the preferences of people , 2012, Machine Learning.
[51] Peter Spyns,et al. Enhancing the Business Analysis Function with Semantics , 2006, OTM Conferences.
[52] Fred A. Hamprecht,et al. Structured Learning from Partial Annotations , 2012, ICML.
[53] Reshma Khemchandani,et al. Knowledge based proximal support vector machines , 2009, Eur. J. Oper. Res..
[54] Emilio Carrizosa,et al. Detecting relevant variables and interactions in supervised classification , 2011, Eur. J. Oper. Res..
[55] Juan-Zi Li,et al. Tree-Structured Conditional Random Fields for Semantic Annotation , 2006, International Semantic Web Conference.
[56] Dan Roth,et al. Learning cost-sensitive active classifiers , 2002, Artif. Intell..
[57] Bart Baesens,et al. Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines , 2007, Eur. J. Oper. Res..
[58] Lawrence Carin,et al. Cost-sensitive feature acquisition and classification , 2007, Pattern Recognit..
[59] Arnold W. M. Smeulders,et al. Active learning using pre-clustering , 2004, ICML.
[60] Kazushi Ikeda,et al. Incremental support vector machines and their geometrical analyses , 2007, Neurocomputing.
[61] Thorsten Joachims,et al. Improved Learning of Structural Support Vector Machines: Training with Latent Variables and Nonlinear Kernels , 2011 .
[62] Chih-Ping Wei,et al. Managing Word Mismatch Problems in Information Retrieval: A Topic-Based Query Expansion Approach , 2007, J. Manag. Inf. Syst..
[63] Gary Geunbae Lee,et al. MMR-based Active Machine Learning for Bio Named Entity Recognition , 2006, NAACL.
[64] Kalina Bontcheva,et al. SVM Based Learning System for Information Extraction , 2004, Deterministic and Statistical Methods in Machine Learning.
[65] Andreas Hotho,et al. A Brief Survey of Text Mining , 2005, LDV Forum.
[66] Sung-Hyon Myaeng,et al. Text Mining for Medical Documents Using a Hidden Markov Model , 2006, AIRS.
[67] Jin-Dong Kim,et al. The GENIA corpus: an annotated research abstract corpus in molecular biology domain , 2002 .
[68] Stefan Rüping,et al. Incremental Learning with Support Vector Machines , 2001, ICDM.
[69] David A. Cohn,et al. Improving generalization with active learning , 1994, Machine Learning.
[70] Thomas Hofmann,et al. Hidden Markov Support Vector Machines , 2003, ICML.
[71] Nuno Escudeiro,et al. D-Confidence: An Active Learning Strategy which Efficiently Identifies Small Classes , 2010, HLT-NAACL 2010.
[72] Jason Weston,et al. Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..
[73] Hemant K. Bhargava,et al. Feature Article - The World Wide Web: Opportunities for Operations Research and Management Science , 1998, INFORMS J. Comput..
[74] Serpil Sayin,et al. Using support vector machines to learn the efficient set in multiple objective discrete optimization , 2009, Eur. J. Oper. Res..
[75] Michael W. Berry,et al. Survey of Text Mining , 2003, Springer New York.
[76] Raymond J. Mooney,et al. Active Learning for Natural Language Parsing and Information Extraction , 1999, ICML.
[77] Daphne Koller,et al. Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..
[78] nhnguyen,et al. Comparisons of Sequence Labeling Algorithms and Extensions , 2007 .
[79] Balaji Padmanabhan,et al. The identification and satisfaction of consumer analysis‐driven information needs of marketers on the WWW , 1998 .
[80] Balaji Padmanabhan,et al. On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities , 2003, Manag. Sci..
[81] Mihai Surdeanu,et al. Robust Information Extraction with Perceptrons , 2007 .
[82] Xiaonan Li,et al. Operations research and data mining , 2008, Eur. J. Oper. Res..
[83] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[84] Joydeep Ghosh,et al. Relationship-Based Clustering and Visualization for High-Dimensional Data Mining , 2003, INFORMS J. Comput..