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Learning large margin classifiers locally and globally

Published: 04 July 2004 Publication History

Abstract

A new large margin classifier, named Maxi-Min Margin Machine (M4) is proposed in this paper. This new classifier is constructed based on both a "local: and a "global" view of data, while the most popular large margin classifier, Support Vector Machine (SVM) and the recently-proposed important model, Minimax Probability Machine (MPM) consider data only either locally or globally. This new model is theoretically important in the sense that SVM and MPM can both be considered as its special case. Furthermore, the optimization of M4 can be cast as a sequential conic programming problem, which can be solved efficiently. We describe the M4 model definition, provide a clear geometrical interpretation, present theoretical justifications, propose efficient solving methods, and perform a series of evaluations on both synthetic data sets and real world benchmark data sets. Its comparison with SVM and MPM also demonstrates the advantages of our new model.

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  • (2018)Boundary-Eliminated Pseudoinverse Linear Discriminant for Imbalanced ProblemsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2017.267623929:6(2581-2594)Online publication date: Jun-2018
  • (2017)Structural Minimax Probability MachineIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2016.254477928:7(1646-1656)Online publication date: Jul-2017
  • (2017)Soft Computing in Remote Sensing ApplicationsProceedings of the National Academy of Sciences, India Section A: Physical Sciences10.1007/s40010-017-0431-087:4(503-517)Online publication date: 7-Dec-2017
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cover image ACM Other conferences
ICML '04: Proceedings of the twenty-first international conference on Machine learning
July 2004
934 pages
ISBN:1581138385
DOI:10.1145/1015330
  • Conference Chair:
  • Carla Brodley
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 July 2004

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View all
  • (2018)Boundary-Eliminated Pseudoinverse Linear Discriminant for Imbalanced ProblemsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2017.267623929:6(2581-2594)Online publication date: Jun-2018
  • (2017)Structural Minimax Probability MachineIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2016.254477928:7(1646-1656)Online publication date: Jul-2017
  • (2017)Soft Computing in Remote Sensing ApplicationsProceedings of the National Academy of Sciences, India Section A: Physical Sciences10.1007/s40010-017-0431-087:4(503-517)Online publication date: 7-Dec-2017
  • (2016)Support vector machine with hypergraph-based pairwise constraintsSpringerPlus10.1186/s40064-016-3315-x5:1Online publication date: 23-Sep-2016
  • (2016)v-Structural Nonparallel Support Vector Machine for Pattern Classification2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW)10.1109/WIW.2016.021(33-36)Online publication date: Oct-2016
  • (2016)Structural Nonparallel Support Vector Machine Based on LSH for Large-Scale Prediction2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2016.0124(839-846)Online publication date: Dec-2016
  • (2016)Structural nonparallel support vector machine for pattern recognitionPattern Recognition10.1016/j.patcog.2016.04.01760:C(296-305)Online publication date: 1-Dec-2016
  • (2016)A local and global classification machine with collaborative mechanismPattern Analysis & Applications10.1007/s10044-014-0410-x19:2(385-396)Online publication date: 1-May-2016
  • (2016)Improving Generalization Abilities of Maximal Average Margin ClassifiersArtificial Neural Networks in Pattern Recognition10.1007/978-3-319-46182-3_3(29-41)Online publication date: 9-Sep-2016
  • (2015)Learning Imbalanced Classifiers Locally and Globally with One-Side Probability MachineNeural Processing Letters10.1007/s11063-014-9370-941:3(311-323)Online publication date: 1-Jun-2015
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