Theory of linear and integer programming
Theory of linear and integer programming
Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Machine Learning
Boosting margin based distance functions for clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Inference on the prediction of ensembles of infinite size
Pattern Recognition
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Classification margin is commonly used for describing the classification capability of a committee of classifiers. In this paper, we study the relation between classification margin and misclassification error, focusing on exploring useful information about misclassification error from the known classification margin. We propose a max-min type bound concerning the minimal misclassification rate, and present some useful properties. Finally, we seek the way to improve classification performance by incorporating the classification margins, and devise an algorithm for improving average classification accuracy based on the proposed bound. Experimental results show the effectiveness of the proposed algorithm and also validate our analytic results.