Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
A robust minimax approach to classification
The Journal of Machine Learning Research
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
Negative Samples Analysis in Relevance Feedback
IEEE Transactions on Knowledge and Data Engineering
Structured large margin machines: sensitive to data distributions
Machine Learning
Structural Support Vector Machine
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Support vector machines based on weighted scatter degree
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Heterogeneous information integration in hierarchical text classification
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A modified large margin classifier in hidden space for face recognition
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Multiview Metric Learning with Global Consistency and Local Smoothness
ACM Transactions on Intelligent Systems and Technology (TIST)
Semi-supervised discriminatively regularized classifier with pairwise constraints
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Generalized locality preserving Maxi-Min Margin Machine
Neural Networks
Three-fold structured classifier design based on matrix pattern
Pattern Recognition
Structural twin support vector machine for classification
Knowledge-Based Systems
A second order cone programming approach for semi-supervised learning
Pattern Recognition
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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.